Showing 1–50 of 85 results for author: Miller, T

Particle dynamics and confinement in moving multi-mirror

Authors: Tal Miller, Eli Gudinetsky, Ilan Be'ery, Ido Barth

Abstract: The moving multi-mirror (MMM) concept mitigates axial losses in magnetic mirrors by using inward-propagating multi-mirror sections to transport escaping particles back toward the central cell. Single-particle simulations are used to establish the underlying dynamics, while a modified rate-equation model provides quantitative estimates of the resulting confinement enhancement. It is found that the… ▽ More The moving multi-mirror (MMM) concept mitigates axial losses in magnetic mirrors by using inward-propagating multi-mirror sections to transport escaping particles back toward the central cell. Single-particle simulations are used to establish the underlying dynamics, while a modified rate-equation model provides quantitative estimates of the resulting confinement enhancement. It is found that the steady-state outgoing flux can be robustly suppressed by several orders of magnitude. The analysis uncovered confinement challenges in the MMM concept, indicating that additional scattering processes are required in both the central cell and the MMM sections to achieve the desired confinement. △ Less

Submitted 5 July, 2026; originally announced July 2026.

Plugging of multi-mirror machines by a traveling rotating magnetic field

Authors: Tal Miller, Eli Gudinetsky, Ilan Be'ery, Ido Barth

Abstract: Axial plugging is a critical challenge for fusion in open-ended magnetic confinement systems. Unlike simple magnetic mirrors, which suffer from direct axial flow, multi-mirror systems utilize a series of aligned magnetic cells to suppress plasma loss; however, the resulting confinement still requires additional plugging to reach Lawson criterion levels. In [T. Miller et al., Phys. Plasmas 30, 0725… ▽ More Axial plugging is a critical challenge for fusion in open-ended magnetic confinement systems. Unlike simple magnetic mirrors, which suffer from direct axial flow, multi-mirror systems utilize a series of aligned magnetic cells to suppress plasma loss; however, the resulting confinement still requires additional plugging to reach Lawson criterion levels. In [T. Miller et al., Phys. Plasmas 30, 072510 (2023)], it was found that applying a traveling and rotating electric field in multi-mirror machines can significantly suppress axial loss due to a selectivity effect induced by the Doppler shift of the ion cyclotron resonance. However, this method is energetically expensive and vulnerable to plasma screening effects. Here, we show that using a traveling, rotating magnetic field can achieve comparable plugging effectiveness while offering better penetration and lower energy costs. Two limiting scenarios, with and without an induced electric field, were considered. The confinement enhancement is calculated using a semi-kinetic rate equation model, in which the rate coefficients are determined from single-particle simulations. While both scenarios exhibit significant confinement enhancement, the scenario without an induced electric field is much more energetically efficient, as it relies on phase-space mixing rather than on energy deposition in the escaping particles. The decoupling of confinement from plasma collisionality enables fusion conditions in the central cell while allowing affordable and efficient confinement enhancement in the multi-mirror sections. △ Less

Submitted 5 July, 2026; v1 submitted 6 March, 2026; originally announced March 2026.

Journal ref: Journal of Plasma Physics , Volume 92 , Issue 3 , June 2026 , E85

Floquet-engineering unveiled by high-harmonic generation

Authors: Cong Zhao, Lucie Jurkovičová, Xiaozhou Zou, Benjamin T. Q. Miller, Suzan Canbas, Zakaria Dahbi, Martin Albrecht, Ondřej Finke, Jaroslav Nejdl, Margarita Khokhlova, Ondřej Hort, Fabrice Catoire, Amelle Zaïr

Abstract: Ultrafast optical control of solids has uncovered new phenomena and advanced non-equilibrium condensed matter physics, where photon dressed electronic states - Floquet Bloch states (FBSs) - emerge under a strong oscillating laser field, also known as Floquet engineering. Although FBSs have been extensively investigated using time and angle resolved photoemission spectroscopy, direct evidence of th… ▽ More Ultrafast optical control of solids has uncovered new phenomena and advanced non-equilibrium condensed matter physics, where photon dressed electronic states - Floquet Bloch states (FBSs) - emerge under a strong oscillating laser field, also known as Floquet engineering. Although FBSs have been extensively investigated using time and angle resolved photoemission spectroscopy, direct evidence of their role in high-harmonic generation spectroscopy (HHGS) has remained elusive. Here, we present combined experimental and theoretical evidence that FBSs can be probed by HHG emission in the wide-bandgap solid magnesium oxide (MgO) driven by few cycle near infrared pulses. Experimentally, we observe clear evidence of FBSs in the HHG yield dependence on the crystal orientation. This specific feature is attributed to nonadiabatic coupling between FBSs and conduction bands near the Brillouin zone edge, where the strong laser field transiently breaks time reversal symmetry. We have confronted the experimental findings with numerical solutions of the time dependent Schrödinger equation, which reproduce the new feature and confirm its Floquet origin. The theoretical results show a coupling inducing a local band structure renormalization and Floquet like hybridization under strong field excitation. It also shows that FBS nonadiabatic dynamics persist in the strong field regime, establishing HHGS as a powerful probe of ultrafast light induced band hybridization in solids. △ Less

Submitted 9 June, 2026; v1 submitted 4 July, 2025; originally announced July 2025.

Semi-analytical solutions of passive scalar transport in generalized Newtonian fluid flow

Authors: Christopher A. Bowers, Cass T. Miller

Abstract: Transport during flow of generalized Newtonian fluids (GNFs) appears often in systems that can be treated in a simplified form as either cylindrical tubes or slit openings between parallel plates. Based on the pioneering work of Taylor, analytical solutions for transport in these simplified systems were derived generally. This includes analytical solutions for advection dominated transport, as wel… ▽ More Transport during flow of generalized Newtonian fluids (GNFs) appears often in systems that can be treated in a simplified form as either cylindrical tubes or slit openings between parallel plates. Based on the pioneering work of Taylor, analytical solutions for transport in these simplified systems were derived generally. This includes analytical solutions for advection dominated transport, as well as a computation of the enhanced molecular diffusion coefficient in low Peclet number systems. The newly derived general solutions for species transport were applied to Cross and Carreau model fluids using a semi-analytical solution for velocity of these fluids. The semi-analytical solutions derived herein were compared to microscale simulations and showed agreement to within the numerical error of those simulations. The semi-analytical transport solutions derived here were developed without assuming any specific fluid rheology, thus these solutions can be applied to other non-Newtonian fluids, such as viscoelastic or viscoplastic fluids, as a straightforward extension of this work. △ Less

Submitted 19 May, 2025; originally announced May 2025.

NeuralPLexer3: Accurate Biomolecular Complex Structure Prediction with Flow Models

Authors: Zhuoran Qiao, Feizhi Ding, Thomas Dresselhaus, Mia A. Rosenfeld, Xiaotian Han, Owen Howell, Aniketh Iyengar, Stephen Opalenski, Anders S. Christensen, Sai Krishna Sirumalla, Frederick R. Manby, Thomas F. Miller III, Matthew Welborn

Abstract: Structure determination is essential to a mechanistic understanding of diseases and the development of novel therapeutics. Machine-learning-based structure prediction methods have made significant advancements by computationally predicting protein and bioassembly structures from sequences and molecular topology alone. Despite substantial progress in the field, challenges remain to deliver structur… ▽ More Structure determination is essential to a mechanistic understanding of diseases and the development of novel therapeutics. Machine-learning-based structure prediction methods have made significant advancements by computationally predicting protein and bioassembly structures from sequences and molecular topology alone. Despite substantial progress in the field, challenges remain to deliver structure prediction models to real-world drug discovery. Here, we present NeuralPLexer3 -- a physics-inspired flow-based generative model that achieves state-of-the-art prediction accuracy on key biomolecular interaction types and improves training and sampling efficiency compared to its predecessors and alternative methodologies. Examined through newly developed benchmarking strategies, NeuralPLexer3 excels in vital areas that are crucial to structure-based drug design, such as physical validity and ligand-induced conformational changes. △ Less

Submitted 18 December, 2024; v1 submitted 14 December, 2024; originally announced December 2024.

Intermittent turbulent gusts lift eagles

Authors: Dipendra Gupta, David Brandes, Michael J Lanzone, Tricia Miller, Gregory P Bewley

Abstract: Turbulence grounds aircraft and combating it in flight requires energy, yet volant wildlife fly effortlessly even on windy days. The nature of the interactions between soaring birds and transient turbulent gusts is not clear, especially when compared with our understanding of flight in larger and steadier airflows during thermal or dynamic soaring. We show that soaring golden eagles (Aquila chrysa… ▽ More Turbulence grounds aircraft and combating it in flight requires energy, yet volant wildlife fly effortlessly even on windy days. The nature of the interactions between soaring birds and transient turbulent gusts is not clear, especially when compared with our understanding of flight in larger and steadier airflows during thermal or dynamic soaring. We show that soaring golden eagles (Aquila chrysaetos) experienced short upward accelerations indicative of preferential engagement with strong and intermittent turbulent updrafts. The vertical accelerations reflect changes in lift that were as large as 25 standard deviations from the mean, or more than three times the acceleration of gravity, and so large as not to be consistent with gust mitigation or avoidance. These extreme events occurred in short bursts that mimic movement with turbulent vortices. The burst statistics and their symmetries approach those of turbulence toward longer timescales. On the shortest timescales, the bursts break the symmetry of small-scale turbulence in favor of upward accelerations that are more intermittent than turbulence. We introduce a simple nonlinear model that predicts the scale at which symmetry breaks and the stronger intermittency on the smaller scales. These findings suggest a ratcheting mechanism on turbulent gusts and constitute the first quantitative evidence in favor of turbulent gust harvesting by wildlife. An implication is that turbulence is so strong and pervasive as to make unsteady and nonlinear aerodynamics an intrinsic and beneficial aspect of both flapping and soaring flight in the atmospheric boundary layer - one that we need to incorporate in our understanding of the energetics of flight. △ Less

Submitted 2 December, 2024; v1 submitted 29 November, 2024; originally announced December 2024.

Comments: 20 Pages, 3 Figures

A Traveling-Wave Parametric Amplifier and Converter

Authors: M. Malnou, B. T. Miller, J. A. Estrada, K. Genter, K. Cicak, J. D. Teufel, J. Aumentado, F. Lecocq

Abstract: High-fidelity qubit measurement is a critical element of all quantum computing architectures. In superconducting systems, qubits are typically measured by probing a readout resonator with a weak microwave tone that must be amplified before reaching the room temperature electronics. Superconducting parametric amplifiers have been widely adopted as the first amplifier in the chain, primarily because… ▽ More High-fidelity qubit measurement is a critical element of all quantum computing architectures. In superconducting systems, qubits are typically measured by probing a readout resonator with a weak microwave tone that must be amplified before reaching the room temperature electronics. Superconducting parametric amplifiers have been widely adopted as the first amplifier in the chain, primarily because of their low noise performance, approaching the quantum limit. However, they require isolators and circulators to route signals up the measurement chain and to protect qubits from amplified noise. While these commercial components are wideband and simple to use, their intrinsic loss, size, and magnetic shielding requirements impact overall measurement efficiency and scalability. Here we report a parametric amplifier that achieves both broadband forward amplification and backward isolation in a single, compact, non-magnetic circuit that could be integrated on chip with superconducting qubits. The approach relies on a nonlinear transmission line that supports traveling-wave parametric amplification of forward propagating signals, and isolation via frequency conversion of backward propagating signals. This traveling-wave parametric amplifier and converter has the potential to reduce the readout hardware overhead when scaling up the size of superconducting quantum computers. △ Less

Submitted 13 March, 2026; v1 submitted 27 June, 2024; originally announced June 2024.

Autoresonant Removal of Fusion Products in Mirror Machines

Authors: Eli Gudinetsky, Tal Miller, Ilan Be'ery, Ido Barth

Abstract: Magnetic confinement fusion reactors produce ash particles that must be removed for efficient operation. It is suggested to use autoresonance (a continuous phase-locking between anharmonic motion and a chirped drive) to remove the ash particles from a magnetic mirror, the simplest magnetic confinement configuration. An analogy to the driven pendulum is established via the guiding center approximat… ▽ More Magnetic confinement fusion reactors produce ash particles that must be removed for efficient operation. It is suggested to use autoresonance (a continuous phase-locking between anharmonic motion and a chirped drive) to remove the ash particles from a magnetic mirror, the simplest magnetic confinement configuration. An analogy to the driven pendulum is established via the guiding center approximation. The full 3D dynamics is simulated for $α$ particles (the byproduct of DT fusion) in agreement with the approximated 1D model. Monte Carlo simulations sampling the phase space of initial conditions are used to quantify the efficiency of the method. The DT fuel particles are out of the bandwidth of the chirped drive and, therefore, stay in the mirror for ongoing fusion. The method is also applicable for advanced, aneutronic reactors, such as p-$^{11}$B. △ Less

Submitted 30 April, 2025; v1 submitted 28 February, 2024; originally announced February 2024.

Comments: 5 pages, 4 figures

Journal ref: E. Gudinetsky, T. Miller, I. Be'ery, and I. Barth, Phys. Rev. Lett. 134, 155101 (2025)

RF plugging of multi-mirror machines

Authors: Tal Miller, Ilan Be'ery, Eli Gudinetsky, Ido Barth

Abstract: One of the main challenges of fusion reactors based on magnetic mirrors is the axial particle loss through the loss cones. In multi-mirror (MM) systems, the particle loss is addressed by adding mirror cells on each end of the central fusion cell. Coulomb collisions in the MM sections serve as the retrapping mechanism for the escaping particles. Unfortunately, the confinement time in this system on… ▽ More One of the main challenges of fusion reactors based on magnetic mirrors is the axial particle loss through the loss cones. In multi-mirror (MM) systems, the particle loss is addressed by adding mirror cells on each end of the central fusion cell. Coulomb collisions in the MM sections serve as the retrapping mechanism for the escaping particles. Unfortunately, the confinement time in this system only scales linearly with the number of cells in the MM sections and requires an unreasonably large number of cells to satisfy the Lawson criterion. Here, it is suggested to reduce the outflow by applying a traveling RF electric field that mainly targets the particles in the outgoing loss cone. The Doppler shift compensates for the detuning of the RF frequency from the ion cyclotron resonance mainly for the escaping particles resulting in a selectivity effect. The transition rates between the different phase space populations are quantified via single-particle calculations and then incorporated into a semi-kinetic rate equations model for the MM system, including the RF effect. It is found that for optimized parameters, the confinement time can scale exponentially with the number of MM cells, orders of magnitude better than a similar MM system of the same length but without the RF plugging, and can satisfy the Lawson criterion for a reasonable system size. △ Less

Submitted 7 March, 2024; v1 submitted 29 June, 2023; originally announced June 2023.

The FluidFlower International Benchmark Study: Process, Modeling Results, and Comparison to Experimental Data

Authors: Bernd Flemisch, Jan M. Nordbotten, Martin Fernø, Ruben Juanes, Holger Class, Mojdeh Delshad, Florian Doster, Jonathan Ennis-King, Jacques Franc, Sebastian Geiger, Dennis Gläser, Christopher Green, James Gunning, Hadi Hajibeygi, Samuel J. Jackson, Mohamad Jammoul, Satish Karra, Jiawei Li, Stephan K. Matthäi, Terry Miller, Qi Shao, Catherine Spurin, Philip Stauffer, Hamdi Tchelepi, Xiaoming Tian , et al. (8 additional authors not shown)

Abstract: Successful deployment of geological carbon storage (GCS) requires an extensive use of reservoir simulators for screening, ranking and optimization of storage sites. However, the time scales of GCS are such that no sufficient long-term data is available yet to validate the simulators against. As a consequence, there is currently no solid basis for assessing the quality with which the dynamics of la… ▽ More Successful deployment of geological carbon storage (GCS) requires an extensive use of reservoir simulators for screening, ranking and optimization of storage sites. However, the time scales of GCS are such that no sufficient long-term data is available yet to validate the simulators against. As a consequence, there is currently no solid basis for assessing the quality with which the dynamics of large-scale GCS operations can be forecasted. To meet this knowledge gap, we have conducted a major GCS validation benchmark study. To achieve reasonable time scales, a laboratory-size geological storage formation was constructed (the "FluidFlower"), forming the basis for both the experimental and computational work. A validation experiment consisting of repeated GCS operations was conducted in the FluidFlower, providing what we define as the true physical dynamics for this system. Nine different research groups from around the world provided forecasts, both individually and collaboratively, based on a detailed physical and petrophysical characterization of the FluidFlower sands. The major contribution of this paper is a report and discussion of the results of the validation benchmark study, complemented by a description of the benchmarking process and the participating computational models. The forecasts from the participating groups are compared to each other and to the experimental data by means of various indicative qualitative and quantitative measures. By this, we provide a detailed assessment of the capabilities of reservoir simulators and their users to capture both the injection and post-injection dynamics of the GCS operations. △ Less

Submitted 9 February, 2023; originally announced February 2023.

Exploring PROTAC cooperativity with coarse-grained alchemical methods

Authors: Huanghao Mai, Matthew H. Zimmer, Thomas F. Miller III

Abstract: Proteolysis targeting chimera (PROTAC) is a novel drug modality that facilitates the degradation of a target protein by inducing proximity with an E3 ligase. In this work, we present a new computational framework to model the cooperativity between PROTAC-E3 binding and PROTAC-target binding principally through protein-protein interactions (PPIs) induced by the PROTAC. Due to the scarcity and low r… ▽ More Proteolysis targeting chimera (PROTAC) is a novel drug modality that facilitates the degradation of a target protein by inducing proximity with an E3 ligase. In this work, we present a new computational framework to model the cooperativity between PROTAC-E3 binding and PROTAC-target binding principally through protein-protein interactions (PPIs) induced by the PROTAC. Due to the scarcity and low resolution of experimental measurements, the physical and chemical drivers of these non-native PPIs remain to be elucidated. We develop a coarse-grained (CG) approach to model interactions in the target-PROTAC-E3 complexes, which enables converged thermodynamic estimations using alchemical free energy calculation methods despite an unconventional scale of perturbations. With minimal parameterization, we successfully capture fundamental principles of cooperativity, including the optimality of intermediate PROTAC linker lengths that originates from configurational entropy. We qualitatively characterize the dependency of cooperativity on PROTAC linker lengths and protein charges and shapes. Minimal inclusion of sequence- and conformation-specific features in our current forcefield, however, limits quantitative modeling to reproduce experimental measurements, but further development of the CG model may allow for efficient computational screening to optimize PROTAC cooperativity. △ Less

Submitted 17 November, 2022; v1 submitted 12 August, 2022; originally announced August 2022.

Molecular-orbital-based Machine Learning for Open-shell and Multi-reference Systems with Kernel Addition Gaussian Process Regression

Authors: Lixue Cheng, Jiace Sun, J. Emiliano Deustua, Vignesh C. Bhethanabotla, Thomas F. Miller III

Abstract: We introduce a novel machine learning strategy, kernel addition Gaussian process regression (KA-GPR), in molecular-orbital-based machine learning (MOB-ML) to learn the total correlation energies of general electronic structure theories for closed- and open-shell systems by introducing a machine learning strategy. The learning efficiency of MOB-ML (KA-GPR) is the same as the original MOB-ML method… ▽ More We introduce a novel machine learning strategy, kernel addition Gaussian process regression (KA-GPR), in molecular-orbital-based machine learning (MOB-ML) to learn the total correlation energies of general electronic structure theories for closed- and open-shell systems by introducing a machine learning strategy. The learning efficiency of MOB-ML (KA-GPR) is the same as the original MOB-ML method for the smallest criegee molecule, which is a closed-shell molecule with multi-reference characters. In addition, the prediction accuracies of different small free radicals could reach the chemical accuracy of 1 kcal/mol by training on one example structure. Accurate potential energy surfaces for the H10 chain (closed-shell) and water OH bond dissociation (open-shell) could also be generated by MOB-ML (KA-GPR). To explore the breadth of chemical systems that KA-GPR can describe, we further apply MOB-ML to accurately predict the large benchmark datasets for closed- (QM9, QM7b-T, GDB-13-T) and open-shell (QMSpin) molecules. △ Less

Submitted 17 July, 2022; originally announced July 2022.

Comments: 9 pages, 7 figures

Molecular Dipole Moment Learning via Rotationally Equivariant Gaussian Process Regression with Derivatives in Molecular-orbital-based Machine Learning

Authors: Jiace Sun, Lixue Cheng, Thomas F. Miller III

Abstract: This study extends the accurate and transferable molecular-orbital-based machine learning (MOB-ML) approach to modeling the contribution of electron correlation to dipole moments at the cost of Hartree-Fock computations. A molecular-orbital-based (MOB) pairwise decomposition of the correlation part of the dipole moment is applied, and these pair dipole moments could be further regressed as a unive… ▽ More This study extends the accurate and transferable molecular-orbital-based machine learning (MOB-ML) approach to modeling the contribution of electron correlation to dipole moments at the cost of Hartree-Fock computations. A molecular-orbital-based (MOB) pairwise decomposition of the correlation part of the dipole moment is applied, and these pair dipole moments could be further regressed as a universal function of molecular orbitals (MOs). The dipole MOB features consist of the energy MOB features and their responses to electric fields. An interpretable and rotationally equivariant Gaussian process regression (GPR) with derivatives algorithm is introduced to learn the dipole moment more efficiently. The proposed problem setup, feature design, and ML algorithm are shown to provide highly-accurate models for both dipole moment and energies on water and fourteen small molecules. To demonstrate the ability of MOB-ML to function as generalized density-matrix functionals for molecular dipole moments and energies of organic molecules, we further apply the proposed MOB-ML approach to train and test the molecules from the QM9 dataset. The application of local scalable GPR with Gaussian mixture model unsupervised clustering (GMM/GPR) scales up MOB-ML to a large-data regime while retaining the prediction accuracy. In addition, compared with literature results, MOB-ML provides the best test MAEs of 4.21 mDebye and 0.045 kcal/mol for dipole moment and energy models, respectively, when training on 110000 QM9 molecules. The excellent transferability of the resulting QM9 models is also illustrated by the accurate predictions for four different series of peptides. △ Less

Submitted 30 May, 2022; originally announced May 2022.

Comments: 11 pages,6 figures

Accurate Molecular-Orbital-Based Machine Learning Energies via Unsupervised Clustering of Chemical Space

Authors: Lixue Cheng, Jiace Sun, Thomas F. Miller III

Abstract: We introduce an unsupervised clustering algorithm to improve training efficiency and accuracy in predicting energies using molecular-orbital-based machine learning (MOB-ML). This work determines clusters via the Gaussian mixture model (GMM) in an entirely automatic manner and simplifies an earlier supervised clustering approach [J. Chem. Theory Comput., 15, 6668 (2019)] by eliminating both the nec… ▽ More We introduce an unsupervised clustering algorithm to improve training efficiency and accuracy in predicting energies using molecular-orbital-based machine learning (MOB-ML). This work determines clusters via the Gaussian mixture model (GMM) in an entirely automatic manner and simplifies an earlier supervised clustering approach [J. Chem. Theory Comput., 15, 6668 (2019)] by eliminating both the necessity for user-specified parameters and the training of an additional classifier. Unsupervised clustering results from GMM have the advantage of accurately reproducing chemically intuitive groupings of frontier molecular orbitals and having improved performance with an increasing number of training examples. The resulting clusters from supervised or unsupervised clustering is further combined with scalable Gaussian process regression (GPR) or linear regression (LR) to learn molecular energies accurately by generating a local regression model in each cluster. Among all four combinations of regressors and clustering methods, GMM combined with scalable exact Gaussian process regression (GMM/GPR) is the most efficient training protocol for MOB-ML. The numerical tests of molecular energy learning on thermalized datasets of drug-like molecules demonstrate the improved accuracy, transferability, and learning efficiency of GMM/GPR over not only other training protocols for MOB-ML, i.e., supervised regression-clustering combined with GPR(RC/GPR) and GPR without clustering. GMM/GPR also provide the best molecular energy predictions compared with the ones from literature on the same benchmark datasets. With a lower scaling, GMM/GPR has a 10.4-fold speedup in wall-clock training time compared with scalable exact GPR with a training size of 6500 QM7b-T molecules. △ Less

Submitted 20 April, 2022; originally announced April 2022.

Comments: 28 pages, 7 figures

Equilibrium-nonequilibrium ring-polymer molecular dynamics for nonlinear spectroscopy

Authors: Tomislav Begušić, Xuecheng Tao, Geoffrey A. Blake, Thomas F. Miller III

Abstract: Two-dimensional Raman and hybrid terahertz/Raman spectroscopic techniques provide invaluable insight into molecular structure and dynamics of condensed-phase systems. However, corroborating experimental results with theory is difficult due to the high computational cost of incorporating quantum-mechanical effects in the simulations. Here, we present the equilibrium-nonequilibrium ring-polymer mole… ▽ More Two-dimensional Raman and hybrid terahertz/Raman spectroscopic techniques provide invaluable insight into molecular structure and dynamics of condensed-phase systems. However, corroborating experimental results with theory is difficult due to the high computational cost of incorporating quantum-mechanical effects in the simulations. Here, we present the equilibrium-nonequilibrium ring-polymer molecular dynamics (RPMD), a practical computational method that can account for nuclear quantum effects on the two-time response function of nonlinear optical spectroscopy. Unlike a recently developed approach based on the double Kubo transformed (DKT) correlation function, our method is exact in the classical limit, where it reduces to the established equilibrium-nonequilibrium classical molecular dynamics method. Using benchmark model calculations, we demonstrate the advantages of the equilibrium-nonequilibrium RPMD over classical and DKT-based approaches. Importantly, its derivation, which is based on the nonequilibrium RPMD, obviates the need for identifying an appropriate Kubo transformed correlation function and paves the way for applying real-time path-integral techniques to multidimensional spectroscopy. △ Less

Submitted 15 March, 2022; v1 submitted 2 February, 2022; originally announced February 2022.

Comments: v2: Added a two-dimensional model system (Fig. 4), updated list of authors, other minor improvements; last 10 pages contain the supplementary material

Measurements of the neutron absorption in supermirror coatings

Authors: D. D. DiJulio, V. Santoro, A. Devishvili, A. Khaplanov, R. Kolevatov, M. Magán, T. M. Miller, G. Muhrer

Abstract: In this work we report on measurements of neutron absorption in supermirror coatings. The measurements were carried out using the SuperADAM instrument at the Institut Laue-Langevin and by measuring the gamma-ray production from m = 3 and m = 4 neutron supermirrors when illuminated by a beam of neutrons. The results provide a valuable validation for recent computational and theoretical work that ca… ▽ More In this work we report on measurements of neutron absorption in supermirror coatings. The measurements were carried out using the SuperADAM instrument at the Institut Laue-Langevin and by measuring the gamma-ray production from m = 3 and m = 4 neutron supermirrors when illuminated by a beam of neutrons. The results provide a valuable validation for recent computational and theoretical work that can be used as input to Monte-Carlo radiation transport calculations for the design of the shielding of neutron scattering instruments. △ Less

Submitted 6 December, 2021; originally announced December 2021.

Molecular Energy Learning Using Alternative Blackbox Matrix-Matrix Multiplication Algorithm for Exact Gaussian Process

Authors: Jiace Sun, Lixue Cheng, Thomas F. Miller III

Abstract: We present an application of the blackbox matrix-matrix multiplication (BBMM) algorithm to scale up the Gaussian Process (GP) training of molecular energies in the molecular-orbital based machine learning (MOB-ML) framework. An alternative implementation of BBMM (AltBBMM) is also proposed to train more efficiently (over four-fold speedup) with the same accuracy and transferability as the original… ▽ More We present an application of the blackbox matrix-matrix multiplication (BBMM) algorithm to scale up the Gaussian Process (GP) training of molecular energies in the molecular-orbital based machine learning (MOB-ML) framework. An alternative implementation of BBMM (AltBBMM) is also proposed to train more efficiently (over four-fold speedup) with the same accuracy and transferability as the original BBMM implementation. The training of MOB-ML was limited to 220 molecules, and BBMM and AltBBMM scale the training of MOB-ML up by over 30 times to 6500 molecules (more than a million pair energies). The accuracy and transferability of both algorithms are examined on the benchmark datasets of organic molecules with 7 and 13 heavy atoms. These lower-scaling implementations of the GP preserve the state-of-the-art learning efficiency in the low-data regime while extending it to the large-data regime with better accuracy than other available machine learning works on molecular energies. △ Less

Submitted 20 September, 2021; originally announced September 2021.

Comments: Preprint, under review

Journal ref: NeurIPS 2021 AI for Science Workshop

OrbNet Denali: A machine learning potential for biological and organic chemistry with semi-empirical cost and DFT accuracy

Authors: Anders S. Christensen, Sai Krishna Sirumalla, Zhuoran Qiao, Michael B. O'Connor, Daniel G. A. Smith, Feizhi Ding, Peter J. Bygrave, Animashree Anandkumar, Matthew Welborn, Frederick R. Manby, Thomas F. Miller III

Abstract: We present OrbNet Denali, a machine learning model for electronic structure that is designed as a drop-in replacement for ground-state density functional theory (DFT) energy calculations. The model is a message-passing neural network that uses symmetry-adapted atomic orbital features from a low-cost quantum calculation to predict the energy of a molecule. OrbNet Denali is trained on a vast dataset… ▽ More We present OrbNet Denali, a machine learning model for electronic structure that is designed as a drop-in replacement for ground-state density functional theory (DFT) energy calculations. The model is a message-passing neural network that uses symmetry-adapted atomic orbital features from a low-cost quantum calculation to predict the energy of a molecule. OrbNet Denali is trained on a vast dataset of 2.3 million DFT calculations on molecules and geometries. This dataset covers the most common elements in bio- and organic chemistry (H, Li, B, C, N, O, F, Na, Mg, Si, P, S, Cl, K, Ca, Br, I) as well as charged molecules. OrbNet Denali is demonstrated on several well-established benchmark datasets, and we find that it provides accuracy that is on par with modern DFT methods while offering a speedup of up to three orders of magnitude. For the GMTKN55 benchmark set, OrbNet Denali achieves WTMAD-1 and WTMAD-2 scores of 7.19 and 9.84, on par with modern DFT functionals. For several GMTKN55 subsets, which contain chemical problems that are not present in the training set, OrbNet Denali produces a mean absolute error comparable to those of DFT methods. For the Hutchison conformers benchmark set, OrbNet Denali has a median correlation coefficient of R^2=0.90 compared to the reference DLPNO-CCSD(T) calculation, and R^2=0.97 compared to the method used to generate the training data (wB97X-D3/def2-TZVP), exceeding the performance of any other method with a similar cost. Similarly, the model reaches chemical accuracy for non-covalent interactions in the S66x10 dataset. For torsional profiles, OrbNet Denali reproduces the torsion profiles of wB97X-D3/def2-TZVP with an average MAE of 0.12 kcal/mol for the potential energy surfaces of the diverse fragments in the TorsionNet500 dataset. △ Less

Submitted 2 July, 2021; v1 submitted 1 July, 2021; originally announced July 2021.

Stern and Diffuse Layer Interactions During Ionic Strength Cycling

Authors: Emily Ma, Jeongmin Kim, HanByul Chang, Paul E. Ohno, Richard J. Jodts, Thomas F. Miller III, Franz M. Geiger

Abstract: Second harmonic generation amplitude and phase measurements are acquired in real time from fused silica:water interfaces that are subjected to ionic strength transitions conducted at pH 5.8. In conjunction with atomistic modeling, we identify correlations between structure in the Stern layer, encoded in the total second-order nonlinear susceptibility, chi(2)tot, and in the diffuse layer, encoded i… ▽ More Second harmonic generation amplitude and phase measurements are acquired in real time from fused silica:water interfaces that are subjected to ionic strength transitions conducted at pH 5.8. In conjunction with atomistic modeling, we identify correlations between structure in the Stern layer, encoded in the total second-order nonlinear susceptibility, chi(2)tot, and in the diffuse layer, encoded in the product of chi(2)tot and the total interfacial potential, phi(0)tot. chi(2)tot:phi(0)tot correlation plots indicate that the dynamics in the Stern and diffuse layers are decoupled from one another under some conditions (large change in ionic strength), while they change in lockstep under others (smaller change in ionic strength) as the ionic strength in the aqueous bulk solution varies. The quantitative structural and electrostatic information obtained also informs on the molecular origin of hysteresis in ionic strength cycling over fused silica. Atomistic simulations suggest a prominent role of contact ion pairs (as opposed to solvent-separated ion pairs) in the Stern layer. Those simulations also indicate that net water alignment is limited to the first 2 nm from the interface, even at 0 M ionic strength, highlighting water's polarization as an important contributor to nonlinear optical signal generation. △ Less

Submitted 5 June, 2021; originally announced June 2021.

Comments: Pe-edited version, 21 pages main text, 6 Figures, Supporting Information available upon request

Informing Geometric Deep Learning with Electronic Interactions to Accelerate Quantum Chemistry

Authors: Zhuoran Qiao, Anders S. Christensen, Matthew Welborn, Frederick R. Manby, Anima Anandkumar, Thomas F. Miller III

Abstract: Predicting electronic energies, densities, and related chemical properties can facilitate the discovery of novel catalysts, medicines, and battery materials. By developing a physics-inspired equivariant neural network, we introduce a method to learn molecular representations based on the electronic interactions among atomic orbitals. Our method, OrbNet-Equi, leverages efficient tight-binding simul… ▽ More Predicting electronic energies, densities, and related chemical properties can facilitate the discovery of novel catalysts, medicines, and battery materials. By developing a physics-inspired equivariant neural network, we introduce a method to learn molecular representations based on the electronic interactions among atomic orbitals. Our method, OrbNet-Equi, leverages efficient tight-binding simulations and learned mappings to recover high fidelity quantum chemical properties. OrbNet-Equi models a wide spectrum of target properties with an accuracy consistently better than standard machine learning methods and a speed orders of magnitude greater than density functional theory. Despite only using training samples collected from readily available small-molecule libraries, OrbNet-Equi outperforms traditional methods on comprehensive downstream benchmarks that encompass diverse main-group chemical processes. Our method also describes interactions in challenging charge-transfer complexes and open-shell systems. We anticipate that the strategy presented here will help to expand opportunities for studies in chemistry and materials science, where the acquisition of experimental or reference training data is costly. △ Less

Submitted 1 April, 2022; v1 submitted 30 May, 2021; originally announced May 2021.

Journal ref: Proceedings of the National Academy of Sciences 119.31 (2022): e2205221119

Rate equations model for multiple magnetic mirrors in various thermodynamic scenarios

Authors: Tal Miller, Ilan Be'ery, Ido Barth

Abstract: Axial particle loss is one of the main challenges for fusion aimed, linear magnetic mirror plasma configurations. One way to mitigate this disadvantage and increase the confinement time is to use a multiple mirrors setup. The idea is to reduce the outgoing flux by collisions in the outer magnetic cells. Here, we develop a semi-kinetic rate equation model for the ions' density dynamics, including s… ▽ More Axial particle loss is one of the main challenges for fusion aimed, linear magnetic mirror plasma configurations. One way to mitigate this disadvantage and increase the confinement time is to use a multiple mirrors setup. The idea is to reduce the outgoing flux by collisions in the outer magnetic cells. Here, we develop a semi-kinetic rate equation model for the ions' density dynamics, including scattering within the magnetic cell and the transmission between neighboring cells. The dominant parameter is the ions' mean free path, which depends on the temperature and density in each cell. The steady-state flow is studied analytically and numerically for three thermodynamic scenarios: isothermal plasma, adiabatic expansion, and constant diffusion. It is found that the confinement time varies about five-fold over the different scenarios, where the adiabatic cooling is the best confining scenario. △ Less

Submitted 14 March, 2022; v1 submitted 20 May, 2021; originally announced May 2021.

Comments: 9 pages, 6 figures

A New Imaginary Term in the 2nd Order Nonlinear Susceptibility from Charged Interfaces

Authors: Emily Ma, Paul E. Ohno, Jeongmin Kim, Yangdongling Dawning Liu, Emilie H. Lozier, Thomas F. Miller III, Hong-Fei Wang, Franz M. Geiger

Abstract: Non-resonant second harmonic generation phase and amplitude measurements obtained from the silica:water interface at varying pH and 0.5 M ionic strength point to the existence of a nonlinear susceptibility term, which we call chi(3)X, that is associated with a 90 deg phase shift. Including this contribution in a model for the total effective second-order nonlinear susceptibility produces reasonabl… ▽ More Non-resonant second harmonic generation phase and amplitude measurements obtained from the silica:water interface at varying pH and 0.5 M ionic strength point to the existence of a nonlinear susceptibility term, which we call chi(3)X, that is associated with a 90 deg phase shift. Including this contribution in a model for the total effective second-order nonlinear susceptibility produces reasonable point estimates for interfacial potentials and second-order nonlinear susceptibilities when chi(3)Xis about 1.5 times chi(3)water. A model without this term and containing only traditional chi(2) and chi(3) terms cannot recapitulate the experimental data. The new model also provides a demonstrated utility for distinguishing apparent differences in the second-order nonlinear susceptibility when the electrolyte is NaCl vs MgSO4, pointing to the possibility of using HD-SHG to investigate ion-specificity in interfacial processes. △ Less

Submitted 6 April, 2021; originally announced April 2021.

Comments: Pre-edited version, 16 Pages main text, 4 Figures, Supporting Information

Journal ref: J. Phys. Chem. Letters (2021)

Turbulence explains the accelerations of an eagle in natural flight

Authors: Kasey M. Laurent, Bob Fogg, Tobias Ginsburg, Casey Halverson, Michael Lanzone, Tricia A. Miller, David W. Winkler, Gregory P. Bewley

Abstract: Turbulent winds and gusts fluctuate on a wide range of timescales from milliseconds to minutes and longer, a range that overlaps the timescales of avian flight behavior, yet the importance of turbulence to avian behavior is unclear. By combining wind speed data with the measured accelerations of a golden eagle (Aquila chrysaetos) flying in the wild, we show that the eagle's accelerations can be ex… ▽ More Turbulent winds and gusts fluctuate on a wide range of timescales from milliseconds to minutes and longer, a range that overlaps the timescales of avian flight behavior, yet the importance of turbulence to avian behavior is unclear. By combining wind speed data with the measured accelerations of a golden eagle (Aquila chrysaetos) flying in the wild, we show that the eagle's accelerations can be explained by a linear interaction with turbulence for timescales between about 1/2 and 10 s. These timescales are comparable to those of typical eagle behaviors, corresponding to between about 1 and 25 wingbeats, and to those of turbulent gusts both larger than the eagle's wingspan and smaller than large-scale atmospheric phenomena such as convection cells. The eagle's accelerations exhibit power spectra and intermittent activity characteristic of turbulence, and increase in proportion to the turbulence intensity. Intermittency results in accelerations that are occasionally several times stronger than gravity, and much larger than the ones we experience while driving, for instance. These imprints of turbulence on the bird's movements need to be further explored to understand the energetics of birds and other volant lifeforms, to improve our own methods of flying through ceaselessly turbulent environments, and to engage airborne wildlife as distributed probes of the changing conditions in the atmosphere. △ Less

Submitted 19 May, 2021; v1 submitted 19 February, 2021; originally announced February 2021.

Comments: 21 pages, 4 figures

Analytical Gradients for Molecular-Orbital-Based Machine Learning

Authors: Sebastian J. R. Lee, Tamara Husch, Feizhi Ding, Thomas F. Miller III

Abstract: Molecular-orbital-based machine learning (MOB-ML) enables the prediction of accurate correlation energies at the cost of obtaining molecular orbitals. Here, we present the derivation, implementation, and numerical demonstration of MOB-ML analytical nuclear gradients which are formulated in a general Lagrangian framework to enforce orthogonality, localization, and Brillouin constraints on the molec… ▽ More Molecular-orbital-based machine learning (MOB-ML) enables the prediction of accurate correlation energies at the cost of obtaining molecular orbitals. Here, we present the derivation, implementation, and numerical demonstration of MOB-ML analytical nuclear gradients which are formulated in a general Lagrangian framework to enforce orthogonality, localization, and Brillouin constraints on the molecular orbitals. The MOB-ML gradient framework is general with respect to the regression technique (e.g., Gaussian process regression or neural networks) and the MOB feature design. We show that MOB-ML gradients are highly accurate compared to other ML methods on the ISO17 data set while only being trained on energies for hundreds of molecules compared to energies and gradients for hundreds of thousands of molecules for the other ML methods. The MOB-ML gradients are also shown to yield accurate optimized structures, at a computational cost for the gradient evaluation that is comparable to Hartree-Fock theory or hybrid DFT. △ Less

Submitted 16 December, 2020; originally announced December 2020.

Multi-task learning for electronic structure to predict and explore molecular potential energy surfaces

Authors: Zhuoran Qiao, Feizhi Ding, Matthew Welborn, Peter J. Bygrave, Daniel G. A. Smith, Animashree Anandkumar, Frederick R. Manby, Thomas F. Miller III

Abstract: We refine the OrbNet model to accurately predict energy, forces, and other response properties for molecules using a graph neural-network architecture based on features from low-cost approximated quantum operators in the symmetry-adapted atomic orbital basis. The model is end-to-end differentiable due to the derivation of analytic gradients for all electronic structure terms, and is shown to be tr… ▽ More We refine the OrbNet model to accurately predict energy, forces, and other response properties for molecules using a graph neural-network architecture based on features from low-cost approximated quantum operators in the symmetry-adapted atomic orbital basis. The model is end-to-end differentiable due to the derivation of analytic gradients for all electronic structure terms, and is shown to be transferable across chemical space due to the use of domain-specific features. The learning efficiency is improved by incorporating physically motivated constraints on the electronic structure through multi-task learning. The model outperforms existing methods on energy prediction tasks for the QM9 dataset and for molecular geometry optimizations on conformer datasets, at a computational cost that is thousand-fold or more reduced compared to conventional quantum-chemistry calculations (such as density functional theory) that offer similar accuracy. △ Less

Submitted 1 December, 2020; v1 submitted 5 November, 2020; originally announced November 2020.

Comments: Accepted for presentation at the Machine Learning for Molecules workshop at NeurIPS 2020

A generalized class of strongly stable and dimension-free T-RPMD integrators

Authors: Jorge L. Rosa-Raíces, Jiace Sun, Nawaf Bou-Rabee, Thomas F. Miller III

Abstract: Recent work shows that strong stability and dimensionality freedom are essential for robust numerical integration of thermostatted ring-polymer molecular dynamics (T-RPMD) and path-integral molecular dynamics (PIMD), without which standard integrators exhibit non-ergodicity and other pathologies [J. Chem. Phys. 151, 124103 (2019); J. Chem. Phys. 152, 104102 (2020)]. In particular, the BCOCB scheme… ▽ More Recent work shows that strong stability and dimensionality freedom are essential for robust numerical integration of thermostatted ring-polymer molecular dynamics (T-RPMD) and path-integral molecular dynamics (PIMD), without which standard integrators exhibit non-ergodicity and other pathologies [J. Chem. Phys. 151, 124103 (2019); J. Chem. Phys. 152, 104102 (2020)]. In particular, the BCOCB scheme, obtained via Cayley modification of the standard BAOAB scheme, features a simple reparametrization of the free ring-polymer sub-step that confers strong stability and dimensionality freedom and has been shown to yield excellent numerical accuracy in condensed-phase systems with large time-steps. Here, we introduce a broader class of T-RPMD numerical integrators that exhibit strong stability and dimensionality freedom, irrespective of the Ornstein-Uhlenbeck friction schedule. In addition to considering equilibrium accuracy and time-step stability as in previous work, we evaluate the integrators on the basis of their rates of convergence to equilibrium and their efficiency at evaluating equilibrium expectation values. Within the generalized class, we find BCOCB to be superior with respect to accuracy and efficiency for various configuration-dependent observables, although other integrators within the generalized class perform better for velocity-dependent quantities. Extensive numerical evidence indicates that the stated performance guarantees hold for the strongly anharmonic case of liquid water. Both analytical and numerical results indicate that BCOCB excels over other known integrators in terms of accuracy, efficiency, and stability with respect to time-step for practical applications. △ Less

Submitted 9 November, 2020; v1 submitted 3 November, 2020; originally announced November 2020.

Comments: Jorge L. Rosa-Raíces and Jiace Sun contributed equally to this work

Journal ref: J. Chem. Phys. 154, 024106 (2021)

Improved accuracy and transferability of molecular-orbital-based machine learning: Organics, transition-metal complexes, non-covalent interactions, and transition states

Authors: Tamara Husch, Jiace Sun, Lixue Cheng, Sebastian J. R. Lee, Thomas F. Miller III

Abstract: Molecular-orbital-based machine learning (MOB-ML) provides a general framework for the prediction of accurate correlation energies at the cost of obtaining molecular orbitals. We demonstrate the importance of preserving physical constraints, including invariance conditions and size consistency, when generating the input for the machine learning model. Numerical improvements are demonstrated for di… ▽ More Molecular-orbital-based machine learning (MOB-ML) provides a general framework for the prediction of accurate correlation energies at the cost of obtaining molecular orbitals. We demonstrate the importance of preserving physical constraints, including invariance conditions and size consistency, when generating the input for the machine learning model. Numerical improvements are demonstrated for different data sets covering total and relative energies for thermally accessible organic and transition-metal containing molecules, non-covalent interactions, and transition-state energies. MOB-ML requires training data from only 1% of the QM7b-T data set (i.e., only 70 organic molecules with seven and fewer heavy atoms) to predict the total energy of the remaining 99% of this data set with sub-kcal/mol accuracy. This MOB-ML model is significantly more accurate than other methods when transferred to a data set comprised of thirteen heavy atom molecules, exhibiting no loss of accuracy on a size intensive (i.e., per-electron) basis. It is shown that MOB-ML also works well for extrapolating to transition-state structures, predicting the barrier region for malonaldehyde intramolecular proton-transfer to within 0.35 kcal/mol when only trained on reactant/product-like structures. Finally, the use of the Gaussian process variance enables an active learning strategy for extending MOB-ML model to new regions of chemical space with minimal effort. We demonstrate this active learning strategy by extending a QM7b-T model to describe non-covalent interactions in the protein backbone-backbone interaction data set to an accuracy of 0.28 kcal/mol. △ Less

Submitted 16 October, 2020; v1 submitted 7 October, 2020; originally announced October 2020.

Sum-Frequency Signals in 2D-Terahertz-Terahertz-Raman Spectroscopy

Authors: Griffin Mead, Haw-Wei Lin, Ioan-Bogdan Magdău, Thomas F. Miller III, Geoffrey A. Blake

Abstract: We demonstrate that halogenated methane 2D-Terahertz Terahertz Raman (2D-TTR) spectra are determined by the complicated structure of the instrument response function (IRF) along $f_1$ and by the molecular coherences along $f_2$. Experimental improvements have helped increase the resolution and dynamic range of the measurements, including accurate THz pulse shape characterization. Sum-frequency exc… ▽ More We demonstrate that halogenated methane 2D-Terahertz Terahertz Raman (2D-TTR) spectra are determined by the complicated structure of the instrument response function (IRF) along $f_1$ and by the molecular coherences along $f_2$. Experimental improvements have helped increase the resolution and dynamic range of the measurements, including accurate THz pulse shape characterization. Sum-frequency excitations convolved with the IRF are found to quantitatively reproduce the 2D-TTR signal. A new Reduced Density Matrix model which incorporates sum-frequency pathways, with linear and harmonic operators fully supports this (re)interpretation of the 2D-TTR spectra. △ Less

Submitted 16 July, 2020; originally announced July 2020.

Comments: Supplemental information available after main text

OrbNet: Deep Learning for Quantum Chemistry Using Symmetry-Adapted Atomic-Orbital Features

Authors: Zhuoran Qiao, Matthew Welborn, Animashree Anandkumar, Frederick R. Manby, Thomas F. Miller III

Abstract: We introduce a machine learning method in which energy solutions from the Schrodinger equation are predicted using symmetry adapted atomic orbitals features and a graph neural-network architecture. \textsc{OrbNet} is shown to outperform existing methods in terms of learning efficiency and transferability for the prediction of density functional theory results while employing low-cost features that… ▽ More We introduce a machine learning method in which energy solutions from the Schrodinger equation are predicted using symmetry adapted atomic orbitals features and a graph neural-network architecture. \textsc{OrbNet} is shown to outperform existing methods in terms of learning efficiency and transferability for the prediction of density functional theory results while employing low-cost features that are obtained from semi-empirical electronic structure calculations. For applications to datasets of drug-like molecules, including QM7b-T, QM9, GDB-13-T, DrugBank, and the conformer benchmark dataset of Folmsbee and Hutchison, \textsc{OrbNet} predicts energies within chemical accuracy of DFT at a computational cost that is thousand-fold or more reduced. △ Less

Submitted 18 January, 2022; v1 submitted 15 July, 2020; originally announced July 2020.

Journal ref: J. Chem. Phys. 153, 124111 (2020)

Nuclear Quantum Effects in Scattering of H and D from Graphene

Authors: Hongyan Jiang, Xuecheng Tao, Marvin Kammler, Feizhi Ding, Alec M. Wodtke, Alexander Kandratsenka, Thomas F. Miller III, Oliver Bünermann

Abstract: We present a detailed study of the nuclear quantum effects in H/D sticking to graphene, comparing classical, quantum and mixed quantum/classical simulations to results of scattering experiments. Agreement with experimentally derived sticking probabilities is improved when nuclear quantum effects are included using ring polymer molecular dynamics. Specifically, the quantum motion of the carbon atom… ▽ More We present a detailed study of the nuclear quantum effects in H/D sticking to graphene, comparing classical, quantum and mixed quantum/classical simulations to results of scattering experiments. Agreement with experimentally derived sticking probabilities is improved when nuclear quantum effects are included using ring polymer molecular dynamics. Specifically, the quantum motion of the carbon atoms enhances sticking, showing that an accurate description of graphene phonons is important to capturing the adsorption dynamics. We also find an inverse H/D isotope effect arising from Newtonian mechanics. △ Less

Submitted 7 July, 2020; originally announced July 2020.

Journal ref: JPCLett 12, 1991-96 (2021)

New high-sensitivity searches for neutrons converting into antineutrons and/or sterile neutrons at the European Spallation Source

Authors: A. Addazi, K. Anderson, S. Ansell, K. Babu, J. Barrow, D. V. Baxter, P. M. Bentley, Z. Berezhiani, R. Bevilacqua, C. Bohm, G. Brooijmans, J. Broussard, R. Biondi, B. Dev, C. Crawford, A. Dolgov, K. Dunne, P. Fierlinger, M. R. Fitzsimmons, A. Fomin, M. Frost, S. Gardner, A. Galindo-Uribarri, E. Golubeva, S. Girmohanta , et al. (70 additional authors not shown)

Abstract: The violation of Baryon Number, $\mathcal{B}$, is an essential ingredient for the preferential creation of matter over antimatter needed to account for the observed baryon asymmetry in the universe. However, such a process has yet to be experimentally observed. The HIBEAM/NNBAR %experiment program is a proposed two-stage experiment at the European Spallation Source (ESS) to search for baryon numbe… ▽ More The violation of Baryon Number, $\mathcal{B}$, is an essential ingredient for the preferential creation of matter over antimatter needed to account for the observed baryon asymmetry in the universe. However, such a process has yet to be experimentally observed. The HIBEAM/NNBAR %experiment program is a proposed two-stage experiment at the European Spallation Source (ESS) to search for baryon number violation. The program will include high-sensitivity searches for processes that violate baryon number by one or two units: free neutron-antineutron oscillation ($n\rightarrow \bar{n}$) via mixing, neutron-antineutron oscillation via regeneration from a sterile neutron state ($n\rightarrow [n',\bar{n}'] \rightarrow \bar{n}$), and neutron disappearance ($n\rightarrow n'$); the effective $Δ\mathcal{B}=0$ process of neutron regeneration ($n\rightarrow [n',\bar{n}'] \rightarrow n$) is also possible. The program can be used to discover and characterise mixing in the neutron, antineutron, and sterile neutron sectors. The experiment addresses topical open questions such as the origins of baryogenesis, the nature of dark matter, and is sensitive to scales of new physics substantially in excess of those available at colliders. A goal of the program is to open a discovery window to neutron conversion probabilities (sensitivities) by up to three orders of magnitude compared with previous searches. The opportunity to make such a leap in sensitivity tests should not be squandered. The experiment pulls together a diverse international team of physicists from the particle (collider and low energy) and nuclear physics communities, while also including specialists in neutronics and magnetics. △ Less

Submitted 8 June, 2020; originally announced June 2020.

High brightness CW electron beams from Superconducting RF photoemission gun

Authors: I. Petrushina, V. N. Litvinenko, Y. Jing, J. Ma, I. Pinayev, K. Shih, G. Wang, Y. H. Wu, J. C. Brutus, Z. Altinbas, A. Di Lieto, P. Inacker, J. Jamilkowski, G. Mahler, M. Mapes, T. Miller, G. Narayan, M. Paniccia, T. Roser, F. Severino, J. Skaritka, L. Smart, K. Smith, V. Soria, Y. Than , et al. (10 additional authors not shown)

Abstract: CW photoinjectors operating at high accelerating gradients promise to revolutionize many areas of science and applications. They can establish the basis for a new generation of monochromatic X-ray free electron lasers, high brightness hadron beams, or a new generation of microchip production. In this letter we report on the record-performing superconducting RF electron gun with… ▽ More CW photoinjectors operating at high accelerating gradients promise to revolutionize many areas of science and applications. They can establish the basis for a new generation of monochromatic X-ray free electron lasers, high brightness hadron beams, or a new generation of microchip production. In this letter we report on the record-performing superconducting RF electron gun with $\textrm{CsK}_{2}\textrm{Sb}$ photocathode. The gun is generating high charge electron bunches (up to 10 nC/bunch) and low transverse emittances, while operating for months with a single photocathode. This achievement opens a new era in generating high-power beams with a very high average brightness. △ Less

Submitted 16 March, 2020; v1 submitted 12 March, 2020; originally announced March 2020.

Journal ref: Phys. Rev. Lett. 124, 244801 (2020)

Development of High Intensity Neutron Source at the European Spallation Source

Authors: V. Santoro, K. H. Andersen, D. D. DiJulio, E. B. Klinkby, T. M. Miller, D. Milstead, G. Muhrer, M. Stroble, A. Takibayev, L. Zanini, O. Zimmer

Abstract: The European Spallation Source being constructed in Lund, Sweden will provide the user community with a neutron source of unprecedented brightness. By 2025, a suite of 15 instruments will be served by a high-brightness moderator system placed above the spallation target. The ESS infrastructure, consisting of the proton linac, the target station, and the instrument halls, allows for implementation… ▽ More The European Spallation Source being constructed in Lund, Sweden will provide the user community with a neutron source of unprecedented brightness. By 2025, a suite of 15 instruments will be served by a high-brightness moderator system placed above the spallation target. The ESS infrastructure, consisting of the proton linac, the target station, and the instrument halls, allows for implementation of a second source below the spallation target. We propose to develop a second neutron source with a high-intensity moderator able to (1) deliver a larger total cold neutron flux, (2) provide high intensities at longer wavelengths in the spectral regions of Cold (4-10 Å), Very Cold (10-40 Å), and Ultra Cold (several 100 Å) neutrons, as opposed to Thermal and Cold neutrons delivered by the top moderator. Offering both unprecedented brilliance, flux, and spectral range in a single facility, this upgrade will make ESS the most versatile neutron source in the world and will further strengthen the leadership of Europe in neutron science. The new source will boost several areas of condensed matter research such as imaging and spin-echo, and will provide outstanding opportunities in fundamental physics investigations of the laws of nature at a precision unattainable anywhere else. At the heart of the proposed system is a volumetric liquid deuterium moderator. Based on proven technology, its performance will be optimized in a detailed engineering study. This moderator will be complemented by secondary sources to provide intense beams of Very- and Ultra-Cold Neutrons. △ Less

Submitted 10 February, 2020; originally announced February 2020.

Comments: 12 pages, 4 figures, proceeding of the 23rd meeting of the International Collaboration on Advanced Neutron Sources (ICANS XXIII) 13th - 18th October 2019 in Chattanooga, Tennessee

Microcanonical rates from ring-polymer molecular dynamics: Direct-shooting, stationary-phase, and maximum-entropy approaches

Authors: Xuecheng Tao, Philip Shushkov, Thomas F. Miller III

Abstract: We address the calculation of microcanonical reaction rates for processes involving significant nuclear quantum effects using ring-polymer molecular dynamics (RPMD), both with and without electronically non-adiabatic transitions. After illustrating the shortcomings of the naive free-particle direct-shooting method, in which the temperature of the internal ring-polymer modes is set to the translati… ▽ More We address the calculation of microcanonical reaction rates for processes involving significant nuclear quantum effects using ring-polymer molecular dynamics (RPMD), both with and without electronically non-adiabatic transitions. After illustrating the shortcomings of the naive free-particle direct-shooting method, in which the temperature of the internal ring-polymer modes is set to the translational energy scale, we investigate alternative strategies based on the expression for the microcanonical rate in terms of the inverse Laplace transform of the thermal reaction rate. It is shown that simple application of the stationary-phase approximation (SPA) dramatically improves the performance of the microcanonical rates using RPMD, particularly in the low-energy region where tunneling dominates. Using the SPA as a Bayesian prior, numerically exact RPMD microcanonical rates are then obtained using maximum entropy inversion of the thermal reaction rates, for both electronically adiabatic and non-adiabatic model systems. Finally, the direct-shooting method is revisited using the SPA-determined temperature for the internal ring-polymer modes, leading to a simple, direct-simulation method with improved accuracy in the tunneling regime. △ Less

Submitted 5 January, 2020; originally announced January 2020.

Physics enhanced neural networks predict order and chaos

Authors: Anshul Choudhary, John F. Lindner, Elliott G. Holliday, Scott T. Miller, Sudeshna Sinha, William L. Ditto

Abstract: Conventional artificial neural networks are powerful tools in science and industry, but they can fail when applied to nonlinear systems where order and chaos coexist. We use neural networks that incorporate the structures and symmetries of Hamiltonian dynamics to predict phase space trajectories even as nonlinear systems transition from order to chaos. We demonstrate Hamiltonian neural networks on… ▽ More Conventional artificial neural networks are powerful tools in science and industry, but they can fail when applied to nonlinear systems where order and chaos coexist. We use neural networks that incorporate the structures and symmetries of Hamiltonian dynamics to predict phase space trajectories even as nonlinear systems transition from order to chaos. We demonstrate Hamiltonian neural networks on the canonical Henon-Heiles system, which models diverse dynamics from astrophysics to chemistry. The power of the technique and the ubiquity of chaos suggest widespread utility. △ Less

Submitted 26 November, 2019; originally announced December 2019.

Comments: 5 pages and 5 figures

Journal ref: Phys. Rev. E 101, 062207 (2020)

Non-Newtonian fluid flow in porous media

Authors: Christopher A. Bowers, Cass T. Miller

Abstract: Single fluid porous medium systems are typically modeled at an averaged length scale termed the macroscale using Darcy's law. Standard approaches for modeling macroscale single fluid phase flow of non-Newtonian fluids extend Darcy's law, using an effective viscosity and assuming that the permeability is invariant. This approach results in a need to determine the effective viscosity for every fluid… ▽ More Single fluid porous medium systems are typically modeled at an averaged length scale termed the macroscale using Darcy's law. Standard approaches for modeling macroscale single fluid phase flow of non-Newtonian fluids extend Darcy's law, using an effective viscosity and assuming that the permeability is invariant. This approach results in a need to determine the effective viscosity for every fluid and flow rate. We use the thermodynamically constrained averaging theory (TCAT) to examine the formulation and closure of a macroscale model for non-Newtonian flow that is consistent with microscale conservation principles and the second law of thermodynamics. A connection between microscale and macroscale quantities is used to calculate interphase momentum transfer for non-Newtonian flow in porous medium systems. Darcy's law is shown to approximate momentum transfer from the fluid phase to the solid phase. This momentum transfer is found to depend on the viscosity at the solid surface. As a consequence of the derived equation for momentum transfer, the commonly called intrinsic permeability is not invariant for non-Newtonian flow, which is an assumption that underlies standard effective viscosity approaches. TCAT is used to derive a macroscale equation relating the flow rate and the pressure gradient dependent upon fluid properties and medium characterization, breaking the need to investigate all flow and composition conditions currently required. This new approach is validated for model systems and used to interpret results from the literature, including an evaluation of conditions under which a transition occurs away from the strictly laminar flow conditions. The results from this work form a basis for more rigorous and realistic modeling of non-Newtonian flow in porous medium systems. △ Less

Submitted 24 November, 2019; originally announced November 2019.

Comments: 26 pages, 10 Figures, research article

Dimension-free path-integral molecular dynamics without preconditioning

Authors: Roman Korol, Jorge L. Rosa-Raíces, Nawaf Bou-Rabee, Thomas F. Miller III

Abstract: Convergence with respect to imaginary-time discretization is an essential part of any path-integral-based calculation. However, an unfortunate property of existing non-preconditioned numerical integration schemes for path-integral molecular dynamics (PIMD) - including ring-polymer molecular dynamics (RPMD) and thermostatted RPMD (T-RPMD) - is that for a given MD timestep, the overlap between the e… ▽ More Convergence with respect to imaginary-time discretization is an essential part of any path-integral-based calculation. However, an unfortunate property of existing non-preconditioned numerical integration schemes for path-integral molecular dynamics (PIMD) - including ring-polymer molecular dynamics (RPMD) and thermostatted RPMD (T-RPMD) - is that for a given MD timestep, the overlap between the exact ring-polymer Boltzmann-Gibbs distribution and that sampled using MD becomes zero in the infinite-bead limit. This has clear implications for hybrid Metropolis Monte-Carlo/MD sampling schemes. We show that these problems can be avoided through the introduction of "dimension-free" numerical integration schemes for which the sampled ring-polymer position distribution has non-zero overlap with the exact distribution in the infinite-bead limit for the case of a harmonic potential. We show that dimension freedom can be achieved via mollification of the forces from the physical potential and with the BCOCB integration scheme. The dimension-free numerical integration schemes yield finite error bounds for a given MD timestep as the number of beads is taken to infinity; these conclusions are proven for harmonic potential and borne out numerically for anharmonic systems, including water. The numerical results for BCOCB are particularly striking, allowing for three-fold increases in the stable timestep for liquid water with respect to the Bussi-Parrinello (OBABO) and Leimkuhler (BAOAB) integrators while introducing negligible errors in the statistical properties and absorption spectrum. Importantly, the dimension-free, non-preconditioned integration schemes introduced here preserve ergodicity and global second-order accuracy, and they remain simple, black-box methods that avoid additional computational costs, tunable parameters, or system-specific implementations. △ Less

Submitted 15 March, 2020; v1 submitted 3 November, 2019; originally announced November 2019.

Comments: 13 pages, 6 figures. New results (for the 2 fs time-step) are added in Figure 1(d), y-axes of figure 6 are rescaled and a few typos are corrected

Journal ref: J. Chem. Phys. 152, 104102 (2020)

Regression-clustering for Improved Accuracy and Training Cost with Molecular-Orbital-Based Machine Learning

Authors: Lixue Cheng, Nikola B. Kovachki, Matthew Welborn, Thomas F. Miller III

Abstract: Machine learning (ML) in the representation of molecular-orbital-based (MOB) features has been shown to be an accurate and transferable approach to the prediction of post-Hartree-Fock correlation energies. Previous applications of MOB-ML employed Gaussian Process Regression (GPR), which provides good prediction accuracy with small training sets; however, the cost of GPR training scales cubically w… ▽ More Machine learning (ML) in the representation of molecular-orbital-based (MOB) features has been shown to be an accurate and transferable approach to the prediction of post-Hartree-Fock correlation energies. Previous applications of MOB-ML employed Gaussian Process Regression (GPR), which provides good prediction accuracy with small training sets; however, the cost of GPR training scales cubically with the amount of data and becomes a computational bottleneck for large training sets. In the current work, we address this problem by introducing a clustering/regression/classification implementation of MOB-ML. In a first step, regression clustering (RC) is used to partition the training data to best fit an ensemble of linear regression (LR) models; in a second step, each cluster is regressed independently, using either LR or GPR; and in a third step, a random forest classifier (RFC) is trained for the prediction of cluster assignments based on MOB feature values. Upon inspection, RC is found to recapitulate chemically intuitive groupings of the frontier molecular orbitals, and the combined RC/LR/RFC and RC/GPR/RFC implementations of MOB-ML are found to provide good prediction accuracy with greatly reduced wall-clock training times. For a dataset of thermalized geometries of 7211 organic molecules of up to seven heavy atoms, both implementations reach chemical accuracy (1 kcal/mol error) with only 300 training molecules, while providing 35000-fold and 4500-fold reductions in the wall-clock training time, respectively, compared to MOB-ML without clustering. The resulting models are also demonstrated to retain transferability for the prediction of large-molecule energies with only small-molecule training data. Finally, it is shown that capping the number of training datapoints per cluster leads to further improvements in prediction accuracy with negligible increases in wall-clock training time. △ Less

Submitted 23 October, 2019; v1 submitted 4 September, 2019; originally announced September 2019.

Comments: 31 pages, 10 figures, with an SI

Energy Conversion via Metal Nanolayers

Authors: Mavis D. Boamah, Emilie H. Lozier, Jeongmin Kim, Paul E. Ohno, Catherine E. Walker, Thomas F. Miller III, Franz M. Geiger

Abstract: Current approaches for electric power generation from nanoscale conducting or semi-conducting layers in contact with moving aqueous droplets are promising as they show efficiencies of around 30 percent, yet, even the most successful ones pose challenges regarding fabrication and scaling. Here, we report stable, all-inorganic single-element structures synthesized in a single step that generate elec… ▽ More Current approaches for electric power generation from nanoscale conducting or semi-conducting layers in contact with moving aqueous droplets are promising as they show efficiencies of around 30 percent, yet, even the most successful ones pose challenges regarding fabrication and scaling. Here, we report stable, all-inorganic single-element structures synthesized in a single step that generate electrical current when alternating salinity gradients flow along its surface in a liquid flow cell. 10 nm to 30 nm thin nanolayers of iron, vanadium, or nickel produce several tens of mV and several microA cm^-2 at aqueous flow velocities of just a few cm s^-1. The principle of operation is strongly sensitive to charge-carrier motion in the thermal oxide nano-overlayer that forms spontaneously in air and then self terminates. Indeed, experiments suggest a role for intra-oxide electron transfer for Fe, V, and Ni nanolayers, as their thermal oxides contain several metal oxidation states, whereas controls using Al or Cr nanolayers, which self-terminate with oxides that are redox inactive under the experimental conditions, exhibit dramatically diminished performance. The nanolayers are shown to generate electrical current in various modes of application with moving liquids, including sliding liquid droplets, salinity gradients in a flowing liquid, and in the oscillatory motion of a liquid without a salinity gradient. △ Less

Submitted 30 July, 2019; originally announced July 2019.

Comments: Pre-edited final version, 16 pages main text, 5 figures

Journal ref: PNAS 2019

Path-accelerated molecular dynamics: Parallel-in-time integration using path integrals

Authors: Jorge L. Rosa-Raíces, Bin Zhang, Thomas F. Miller III

Abstract: Massively parallel computer architectures create new opportunities for the performance of long-timescale molecular dynamics (MD) simulations. Here, we introduce the path-accelerated molecular dynamics (PAMD) method that takes advantage of distributed computing to reduce the wall-clock time of MD simulation via parallelization with respect to MD timesteps. The marginal distribution for the time evo… ▽ More Massively parallel computer architectures create new opportunities for the performance of long-timescale molecular dynamics (MD) simulations. Here, we introduce the path-accelerated molecular dynamics (PAMD) method that takes advantage of distributed computing to reduce the wall-clock time of MD simulation via parallelization with respect to MD timesteps. The marginal distribution for the time evolution of a system is expressed in terms of a path integral, enabling the use of path sampling techniques to numerically integrate MD trajectories. By parallelizing the evaluation of the path action with respect to time and by initializing the path configurations from a non-equilibrium distribution, the algorithm enables significant speedups in terms of the length of MD trajectories that can be integrated in a given amount of wall-clock time. The method is demonstrated for Brownian dynamics, although it is generalizable to other stochastic equations of motion including open systems. We apply the method to two simple systems, a harmonic oscillator and a Lennard-Jones liquid, and we show that in comparison to the conventional Euler integration scheme for Brownian dynamics, the new method can reduce the wall-clock time for integrating trajectories of a given length by more than three orders of magnitude in the former system and more than two in the latter. This new method for parallelizing MD in the dimension of time can be trivially combined with algorithms for parallelizing the MD force evaluation to achieve further speedup. △ Less

Submitted 22 July, 2019; originally announced July 2019.

Journal ref: J. Chem. Phys. 151, 164120 (2019)

Cayley modification for strongly stable path-integral and ring-polymer molecular dynamics

Authors: Roman Korol, Nawaf Bou-Rabee, Thomas F. Miller III

Abstract: Path-integral-based molecular dynamics (MD) simulations are widely used for the calculation of numerically exact quantum Boltzmann properties and approximate dynamical quantities. A nearly universal feature of MD numerical integration schemes for equations of motion based on imaginary-time path integrals is the use of harmonic normal modes for the exact evolution of the free ring-polymer positions… ▽ More Path-integral-based molecular dynamics (MD) simulations are widely used for the calculation of numerically exact quantum Boltzmann properties and approximate dynamical quantities. A nearly universal feature of MD numerical integration schemes for equations of motion based on imaginary-time path integrals is the use of harmonic normal modes for the exact evolution of the free ring-polymer positions and momenta. In this work, we demonstrate that this standard practice creates numerical artifacts. In the context of conservative (i.e., microcanonical) equations of motion, it leads to numerical instability. In the context of thermostatted (i.e., canonical) equations of motion, it leads to non-ergodicity of the sampling. These pathologies are generally proven to arise at integration timesteps that depend only on the system temperature and the number of ring-polymer beads, and they are numerically demonstrated for the cases of conventional ring-polymer molecular dynamics (RPMD) and thermostatted RPMD (TRPMD). Furthermore, it is demonstrated that these numerical artifacts are removed via replacement of the exact free ring-polymer evolution with a second-order approximation based on the Cayley transform. The Cayley modification introduced here can immediately be employed with almost every existing integration scheme for path-integral-based molecular dynamics - including path-integral MD (PIMD), RPMD, TRPMD, and centroid MD - providing strong symplectic stability and ergodicity to the numerical integration, at no penalty in terms of computational cost, algorithmic complexity, or accuracy of the overall MD timestep. Furthermore, it is shown that the improved numerical stability of the Cayley modification allows for the use of larger MD timesteps. We suspect that the Cayley modification will therefore find useful application in many future path-integral-based MD simulations. △ Less

Submitted 24 September, 2019; v1 submitted 18 July, 2019; originally announced July 2019.

Comments: 12 pages, 7 figures. Condition (37) is tightened (due to a sign error fix)

Journal ref: J. Chem. Phys. 151, 124103 (2019)

Interpretation of the THz-THz-Raman Spectrum of Bromoform

Authors: Ioan B. Magdau, Griffin J. Mead, Geoffrey A. Blake, Thomas F. Miller III

Abstract: Nonlinear THz-THz-Raman (TTR) liquid spectroscopy offers new possibilities for studying and understanding condensed-phase chemical dynamics. Although TTR spectra carry rich information about the systems under study, the response is encoded in a three-point correlation function comprising of both dipole and polarizability elements. Theoretical methods are necessary for the interpretation of the exp… ▽ More Nonlinear THz-THz-Raman (TTR) liquid spectroscopy offers new possibilities for studying and understanding condensed-phase chemical dynamics. Although TTR spectra carry rich information about the systems under study, the response is encoded in a three-point correlation function comprising of both dipole and polarizability elements. Theoretical methods are necessary for the interpretation of the experimental results. In this work, we study the liquid-phase dynamics of bromoform, a polarizable molecule with a strong TTR response. Previous work based on reduced density matrix (RDM) simulations suggests that unusually large multi-quanta dipole matrix elements are needed to understand the measured spectrum of bromoform. Here, we demonstrate that a self-consistent definition of the time coordinates with respect to the reference pulse leads to a simplified experimental spectrum. Furthermore, we analytically derive a parametrization for the RDM model by integrating the dipole and polarizability elements to the 4th order in the normal modes, and we enforce inversion symmetry in the calculations by numerically cancelling the components of the response that are even with respect to the field. The resulting analysis eliminates the need to invoke large multi-quanta dipole matrix elements to fit the experimental spectrum; instead, the experimental spectrum is recovered using RDM simulations with dipole matrix parameters that are in agreement with independent ab initio calculations. The fundamental interpretation of the TTR signatures in terms of coupled intramolecular vibrational modes remains unchanged from the previous work. △ Less

Submitted 24 July, 2019; v1 submitted 30 May, 2019; originally announced May 2019.

Journal ref: J. Phys. Chem. A 2019

Analytical Gradients for Projection-Based Wavefunction-in-DFT Embedding

Authors: Sebastian J. R. Lee, Feizhi Ding, Frederick R. Manby, Thomas F. Miller III

Abstract: Projection-based embedding provides a simple, robust, and accurate approach for describing a small part of a chemical system at the level of a correlated wavefunction method while the remainder of the system is described at the level of density functional theory. Here, we present the derivation, implementation, and numerical demonstration of analytical nuclear gradients for projection-based wavefu… ▽ More Projection-based embedding provides a simple, robust, and accurate approach for describing a small part of a chemical system at the level of a correlated wavefunction method while the remainder of the system is described at the level of density functional theory. Here, we present the derivation, implementation, and numerical demonstration of analytical nuclear gradients for projection-based wavefunction-in-density functional theory (WF-in-DFT) embedding. The gradients are formulated in the Lagrangian framework to enforce orthogonality, localization, and Brillouin constraints on the molecular orbitals. An important aspect of the gradient theory is that WF contributions to the total WF-in-DFT gradient can be simply evaluated using existing WF gradient implementations without modification. Another simplifying aspect is that Kohn-Sham (KS) DFT contributions to the projection-based embedding gradient do not require knowledge of the WF calculation beyond the relaxed WF density. Projection-based WF-in-DFT embedding gradients are thus easily generalized to any combination of WF and KS-DFT methods. We provide numerical demonstration of the method for several applications, including calculation of a minimum energy pathway for a hydride transfer in a cobalt-based molecular catalyst using the nudged-elastic-band method at the CCSD-in-DFT level of theory, which reveals large differences from the transition state geometry predicted using DFT. △ Less

Submitted 19 August, 2019; v1 submitted 14 March, 2019; originally announced March 2019.

Comments: 15 pages, 4 figures

Journal ref: J. Chem. Phys. 151, 064112 (2019)

A Universal Density Matrix Functional from Molecular Orbital-Based Machine Learning: Transferability across Organic Molecules

Authors: Lixue Cheng, Matthew Welborn, Anders S. Christensen, Thomas F. Miller III

Abstract: We address the degree to which machine learning can be used to accurately and transferably predict post-Hartree-Fock correlation energies. Refined strategies for feature design and selection are presented, and the molecular-orbital-based machine learning (MOB-ML) method is applied to several test systems. Strikingly, for the MP2, CCSD, and CCSD(T) levels of theory, it is shown that the thermally a… ▽ More We address the degree to which machine learning can be used to accurately and transferably predict post-Hartree-Fock correlation energies. Refined strategies for feature design and selection are presented, and the molecular-orbital-based machine learning (MOB-ML) method is applied to several test systems. Strikingly, for the MP2, CCSD, and CCSD(T) levels of theory, it is shown that the thermally accessible (350 K) potential energy surface for a single water molecule can be described to within 1 millihartree using a model that is trained from only a single reference calculation at a randomized geometry. To explore the breadth of chemical diversity that can be described, MOB-ML is also applied to a new dataset of thermalized (350 K) geometries of 7211 organic models with up to seven heavy atoms. In comparison with the previously reported $Δ$-ML method, MOB-ML is shown to reach chemical accuracy with three-fold fewer training geometries. Finally, a transferability test in which models trained for seven-heavy-atom systems are used to predict energies for thirteen-heavy-atom systems reveals that MOB-ML reaches chemical accuracy with 36-fold fewer training calculations than $Δ$-ML (140 versus 5000 training calculations). △ Less

Submitted 4 April, 2019; v1 submitted 10 January, 2019; originally announced January 2019.

Comments: 8 pages, 3 figures

Journal ref: J. Chem. Phys. 150, 131103 (2019)

Even-handed subsystem selection in projection-based embedding

Authors: Matthew Welborn, Frederick R. Manby, Thomas F. Miller III

Abstract: Projection-based embedding offers a simple framework for embedding correlated wavefunction methods in density functional theory. Partitioning between the correlated wavefunction and density functional subsystems is performed in the space of localized molecular orbitals. However, during a large geometry change--such as a chemical reaction--the nature of these localized molecular orbitals, as well a… ▽ More Projection-based embedding offers a simple framework for embedding correlated wavefunction methods in density functional theory. Partitioning between the correlated wavefunction and density functional subsystems is performed in the space of localized molecular orbitals. However, during a large geometry change--such as a chemical reaction--the nature of these localized molecular orbitals, as well as their partitioning into the two subsystems, can change dramatically. This can lead to unphysical cusps and even discontinuities in the potential energy surface. In this work, we present an even-handed framework for localized orbital partitioning that ensures consistent subsystems across a set of molecular geometries. We illustrate this problem and the even-handed solution with a simple example of an SN2 reaction. Applications to a nitrogen umbrella flip in a cobalt-based CO2 reduction catalyst and to the binding of CO to Cu clusters are presented. In both cases, we find that even-handed partitioning enables chemically accurate embedding with modestly-sized embedded regions for systems in which previous partitioning strategies are problematic. △ Less

Submitted 9 September, 2018; originally announced September 2018.

Comments: 11 pages, 5 figures

Journal ref: J. Chem. Phys., 2018, 149, 144101

An Adaptive Volumetric Flux Boundary Condition for Lattice Boltzmann Methods

Authors: James E. McClure, Zhe Li, Adrian P. Sheppard, Cass T. Miller

Abstract: This paper presents a spatially and temporally adaptive boundary condition to specify the volumetric flux for lattice Boltzmann methods. The approach differs from standard velocity boundary conditions because it allows the velocity to vary over the boundary region provided that the total flux through the boundary satisfies a prescribed constraint, which is a typical scenario for laboratory experim… ▽ More This paper presents a spatially and temporally adaptive boundary condition to specify the volumetric flux for lattice Boltzmann methods. The approach differs from standard velocity boundary conditions because it allows the velocity to vary over the boundary region provided that the total flux through the boundary satisfies a prescribed constraint, which is a typical scenario for laboratory experimental studies. This condition allows the boundary pressure to adjust dynamically to yield a specified boundary flow rate as a means to avoid unphysical mismatch between the boundary velocity and the interior flow field that can arise when a constant velocity boundary condition is applied. The method is validated for simulation of one- and two-fluid flow in complex materials, with conditions determined to match typical experiments used to study flow in porous media. △ Less

Submitted 27 June, 2018; originally announced June 2018.

Transferability in Machine Learning for Electronic Structure via the Molecular Orbital Basis

Authors: Matthew Welborn, Lixue Cheng, Thomas F. Miller III

Abstract: We present a machine learning (ML) method for predicting electronic structure correlation energies using Hartree-Fock input.The total correlation energy is expressed in terms of individual and pair contributions from occupied molecular orbitals, and Gaussian process regression is used to predict these contributions from a feature set that is based on molecular orbital properties, such as Fock, Cou… ▽ More We present a machine learning (ML) method for predicting electronic structure correlation energies using Hartree-Fock input.The total correlation energy is expressed in terms of individual and pair contributions from occupied molecular orbitals, and Gaussian process regression is used to predict these contributions from a feature set that is based on molecular orbital properties, such as Fock, Coulomb, and exchange matrix elements. With the aim of maximizing transferability across chemical systems and compactness of the feature set, we avoid the usual specification of ML features in terms of atom- or geometry-specific information, such atom/element-types, bond-types, or local molecular structure. ML predictions of MP2 and CCSD energies are presented for a range of systems, demonstrating that the method maintains accuracy while providing transferability both within and across chemical families; this includes predictions for molecules with atom-types and elements that are not included in the training set. The method holds promise both in its current form and as a proof-of-principle for the use of ML in the design of generalized density-matrix functionals. △ Less

Submitted 26 July, 2018; v1 submitted 31 May, 2018; originally announced June 2018.

Comments: 8 pages, 5 figures

Journal ref: J. Chem. Theory Comput., 2018, 14 (9), 4772

A geometric state function for two-fluid flow in porous media

Authors: James E. McClure, Ryan T. Armstrong, Mark A. Berrill, Steffen Schlüter, Steffen Berg, William G. Gray, Cass T. Miller

Abstract: Models that describe two-fluid flow in porous media suffer from a widely-recognized problem that the constitutive relationships used to predict capillary pressure as a function of the fluid saturation are non-unique, thus requiring a hysteretic description. As an alternative to the traditional perspec- tive, we consider a geometrical description of the capillary pressure, which relates the average… ▽ More Models that describe two-fluid flow in porous media suffer from a widely-recognized problem that the constitutive relationships used to predict capillary pressure as a function of the fluid saturation are non-unique, thus requiring a hysteretic description. As an alternative to the traditional perspec- tive, we consider a geometrical description of the capillary pressure, which relates the average mean curvature, the fluid saturation, the interfacial area between fluids, and the Euler characteristic. The state equation is formulated using notions from algebraic topology and cast in terms of measures of the macroscale state. Synchrotron-based X-ray micro-computed tomography (μCT) and high- resolution pore-scale simulation is applied to examine the uniqueness of the proposed relationship for six different porous media. We show that the geometric state function is able to characterize the microscopic fluid configurations that result from a wide range of simulated flow conditions in an averaged sense. The geometric state function can serve as a closure relationship within macroscale models to effectively remove hysteretic behavior attributed to the arrangement of fluids within a porous medium. This provides a critical missing component needed to enable a new generation of higher fidelity models to describe two-fluid flow in porous media. △ Less

Submitted 24 May, 2018; originally announced May 2018.

Journal ref: Phys. Rev. Fluids 3, 084306 (2018)

Fast readout algorithm for cylindrical beam position monitors providing good accuracy for particle bunches with large offsets

Authors: P. Thieberger, D. Gassner, R. Hulsart, R. Michnoff, T. Miller, M. Minty, Z. Sorrell, A. Bartnik

Abstract: A simple, analytically correct algorithm is developed for calculating pencil beam coordinates using the signals from an ideal cylindrical particle beam position monitor (BPM) with four pickup electrodes (PUEs) of infinitesimal widths. The algorithm is then applied to simulations of realistic BPMs with finite width PUEs. Surprisingly small deviations are found. Simple empirically determined correct… ▽ More A simple, analytically correct algorithm is developed for calculating pencil beam coordinates using the signals from an ideal cylindrical particle beam position monitor (BPM) with four pickup electrodes (PUEs) of infinitesimal widths. The algorithm is then applied to simulations of realistic BPMs with finite width PUEs. Surprisingly small deviations are found. Simple empirically determined correction terms reduce the deviations even further. The algorithm is then used to study the impact of beam-size upon the precision of BPMs in the non-linear region. As an example of the data acquisition speed advantage, a FPGA-based BPM readout implementation of the new algorithm has been developed and characterized. Finally,the algorithm is tested with BPM data from the Cornell Preinjector. △ Less

Submitted 24 March, 2018; v1 submitted 21 November, 2017; originally announced November 2017.

Comments: 21 pages, 17 figures

Enhancing Cation Diffusion and Suppressing Anion Diffusion via Lewis-Acidic Polymer Electrolytes

Authors: Brett M. Savoie, Michael A. Webb, Thomas F. Miller III

Abstract: Solid polymer electrolytes (SPE) have the potential to increase both the energy density and stability of lithium-based batteries, but low Li-ion conductivity remains a barrier to technological viability. SPEs are designed to maximize Li-ion diffusivity relative to the anion, while maintaining sufficient salt solubility. It is thus remarkable that polyethylene oxide (PEO), the most widely used SPE,… ▽ More Solid polymer electrolytes (SPE) have the potential to increase both the energy density and stability of lithium-based batteries, but low Li-ion conductivity remains a barrier to technological viability. SPEs are designed to maximize Li-ion diffusivity relative to the anion, while maintaining sufficient salt solubility. It is thus remarkable that polyethylene oxide (PEO), the most widely used SPE, exhibits Li-ion diffusivity that is an order of magnitude smaller than that of typical counter-ions, such as TFSI, at moderate salt concentrations. Here, we show that Lewis-basic polymers like PEO intrinsically favor slow cation and rapid anion diffusion while this relationship can be reversed in Lewis-acidic polymers. Using molecular dynamics (MD) simulations, Lewis-acidic polyboranes are identified that achieve up to a ten-fold increase in Li-ion diffusivity and a significant decrease in anion diffusivity, relative to PEO. The results for this new class of Lewis-acidic SPEs illustrate a general principle for increasing Li-ion diffusivity and transference number with polymer chemistries that exhibit weaker cation and stronger anion coordination. △ Less

Submitted 20 October, 2016; originally announced October 2016.

Journal ref: J. Phys. Chem. Lett., 2017, 8, 641-646