Showing 1–50 of 82 results for author: Ye, H

MWF-MIMOSA for efficient simultaneous relaxometry and myelin water fraction mapping

Authors: Yuting Chen, Yohan Jun, Hyeong-Geol Shin, Shizhuo Li, Shohei Fujita, Xingwang Yong, Jiye Kim, Jongho Lee, Gian Franco Piredda, Tom Hilbert, Aneri Bhatt, Susie Y. Huang, Huafeng Liu, Huihui Ye, Shahin Nasr, Borjan Gagoski, Kwok-Shing Chan, Berkin Bilgic

Abstract: Quantitative magnetic resonance imaging (qMRI) provides improved sensitivity and specificity to tissue composition and pathological alterations compared with conventional contrast-weighted imaging. Among various qMRI biomarkers, myelin water imaging is of particular interest because myelin plays a central role in brain function and its alteration is closely associated with many neurological diseas… ▽ More Quantitative magnetic resonance imaging (qMRI) provides improved sensitivity and specificity to tissue composition and pathological alterations compared with conventional contrast-weighted imaging. Among various qMRI biomarkers, myelin water imaging is of particular interest because myelin plays a central role in brain function and its alteration is closely associated with many neurological diseases. However, conventional myelin water fraction (MWF) mapping techniques are often limited by long scan times, low spatial resolution, reduced signal-to-noise ratio (SNR), and high specific absorption rate (SAR). Here, we propose MWF-MIMOSA for efficient simultaneous T1, T2, T2* mapping, magnetic susceptibility source separation, and MWF estimation. To achieve this, multi-contrast and multi-slice zero-shot self-supervised learning (MZS-SSL) was used to jointly reconstruct whole-brain complex-valued images. To improve computational efficiency of the parameter estimation step, a multilayer perceptron (MLP) was trained within the GACELLE GPU-accelerated parameter estimation framework to circumvent the computationally intensive Bloch simulation process, resulting in a >100-fold computational speed-up in MWF estimation. Numerical simulations were performed to evaluate the accuracy and precision of MWF-MIMOSA, and in-vivo results further demonstrated its robustness. Comparison with existing myelin water imaging methods showed that MWF-MIMOSA is highly correlated with established approaches, while providing complementary quantitative parameter maps at higher spatial resolution and with shorter scan times. Notably, simultaneous multi-parametric mapping was achieved in 5 min at 1 mm isotropic resolution, and in 10 min at 0.7 mm isotropic resolution. These results demonstrate the potential of MWF-MIMOSA for fast, high-resolution simultaneous relaxometry and myelin water imaging. △ Less

Submitted 30 July, 2026; originally announced July 2026.

Comments: 43 pages, 16 figures

MSC Class: 94A08; 92C55; 68T07

Wavefunction-based periodic quantum chemistry

Authors: Hong-Zhou Ye, Timothy C. Berkelbach

Abstract: High-accuracy molecular quantum chemistry offers a promising toolbox for applications to condensed-phase systems, but this field is difficult to enter due to its combination of topics from molecular quantum chemistry, solid-state physics, and numerical methods. Aiming to ease this transition, we present a comprehensive, pedagogical tutorial on periodic quantum chemistry calculations, using both me… ▽ More High-accuracy molecular quantum chemistry offers a promising toolbox for applications to condensed-phase systems, but this field is difficult to enter due to its combination of topics from molecular quantum chemistry, solid-state physics, and numerical methods. Aiming to ease this transition, we present a comprehensive, pedagogical tutorial on periodic quantum chemistry calculations, using both mean-field and correlated theories. The subtleties of periodic Coulomb interactions are discussed in detail, focusing on the Ewald summation approach. We describe the two most popular periodic, one-electron basis functions---plane waves and periodic linear combinations of atomic orbitals---and we give formulas for all Hamiltonian integrals. Next, we explain the use of $k$-point sampling as a symmetry adaptation of supercell basis functions and the associated savings in computational costs as well as the use of density fitting and related approximations to further reduce costs. We present the working equations of a few example periodic quantum chemistry methods, including Hartree-Fock theory, perturbation theory, and coupled-cluster theory, and we discuss their finite-size errors and convergence to the physically relevant thermodynamic limit. Finally, we briefly discuss local correlation and quantum embedding theories, which are especially appropriate for periodic systems due to their lattice translational symmetries. △ Less

Submitted 30 July, 2026; originally announced July 2026.

Comments: 22 pages, 6 figures

Correlation-consistent Gaussian basis sets for copper solids from material-constrained atomic optimization

Authors: Jincheng Yu, Xiaoyu Zhang, Min-Ye Zhang, Yu Cao, Qiming Sun, Hong-Zhou Ye

Abstract: Correlation-consistent Gaussian basis sets are central to systematic molecular quantum chemistry, but their direct use in periodic solids is often limited by severe linear dependence from diffuse atomically optimized primitives. This problem is particularly acute for metallic and metal-containing systems, where reliable complete-basis-set (CBS) extrapolation is needed for correlated-wavefunction b… ▽ More Correlation-consistent Gaussian basis sets are central to systematic molecular quantum chemistry, but their direct use in periodic solids is often limited by severe linear dependence from diffuse atomically optimized primitives. This problem is particularly acute for metallic and metal-containing systems, where reliable complete-basis-set (CBS) extrapolation is needed for correlated-wavefunction benchmarks. We introduce material-constrained atomic optimization (MCAO), a basis-set optimization framework that preserves the atomic and correlation-consistent character of Gaussian basis sets while penalizing large overlap-matrix condition numbers in representative solids. As a proof of concept, we generate Dunning-style MCAO-cc-pVXZ basis sets (X = D, T, Q) for Cu with all-electron, scalar-relativistic all-electron, effective core potential (ECP), and pseudopotential treatments. The resulting basis sets remain numerically stable for Cu solids and surfaces while reproducing molecular Cu dimer energetics and plane-wave reference properties of bulk Cu. CBS-extrapolated random-phase approximation calculations further enable a controlled assessment of pseudopotential, relativistic, and basis-set errors in bulk Cu and CO adsorption on Cu(111), providing scalar-relativistic all-electron Gaussian-basis benchmarks for the CO adsorption puzzle. △ Less

Submitted 13 July, 2026; originally announced July 2026.

Adsorption energies and decomposition barrier heights for ethylene carbonate on the surface of lithium from cluster-based quantum chemistry

Authors: Ethan A. Vo, Hung T. Vuong, Zachary K. Goldsmith, Hong-Zhou Ye, Yujing Wei, Sohang Kundu, Ardavan Farahvash, Garvit Agarwal, Richard A. Friesner, Timothy C. Berkelbach

Abstract: For ethylene carbonate on the (100) surface of lithium, we calculate the adsorption energy in two binding motifs as well as the barrier height for a ring-opening decomposition reaction. We validate a scheme for producing results in the thermodynamic limit by correcting results obtained on finite lithium clusters containing only 40-100 atoms, which enables the use of hybrid density functionals, the… ▽ More For ethylene carbonate on the (100) surface of lithium, we calculate the adsorption energy in two binding motifs as well as the barrier height for a ring-opening decomposition reaction. We validate a scheme for producing results in the thermodynamic limit by correcting results obtained on finite lithium clusters containing only 40-100 atoms, which enables the use of hybrid density functionals, the random-phase approximation, and correlated wavefunction theories such as coupled-cluster theory and auxiliary-field quantum Monte Carlo. We find that the high-level theories agree to within 2-5 kcal/mol and can therefore serve as benchmarks for more affordable methods. Using our reference data, we demonstrate that generalized gradient approximation functionals, such as PBE, are not sufficiently accurate for reaction barrier heights, and we identify $ω$B97X-V as an especially promising functional for the interfacial chemistry of electrolyte solvents at lithium metal anodes. △ Less

Submitted 23 March, 2026; originally announced March 2026.

Comments: 7 pages, 4 figures, plus Supplementary Material

The Python Simulations of Chemistry Framework: 10 years of an open-source quantum chemistry project

Authors: Qiming Sun, Matthew R Hermes, Xiaojie Wu, Huanchen Zhai, Xing Zhang, Abdelrahman M. Ahmed, Juan José Aucar, Oliver J. Backhouse, Samragni Banerjee, Peng Bao, Nikolay A. Bogdanov, Kyle Bystrom, Frédéric Chapoton, Ning-Yuan Chen, Ivan Yu. Chernyshov, Helen S. Clifford, Sander Cohen-Janes, Zhi-Hao Cui, Yann D. Damour, Nike Dattani, Linus Bjarne Dittmer, Sebastian Ehlert, Janus Juul Eriksen, Francesco A. Evangelista, Simon A. Ewing , et al. (78 additional authors not shown)

Abstract: Over the past decade, the Python-based Simulations of Chemistry Framework (PySCF) has developed into a widely used open-source platform for electronic structure theory and quantum chemical method development. This article reviews the major advances since the previous overview in 2020, covering new modules and methodology, infrastructure changes, and performance benchmarks. Over the past decade, the Python-based Simulations of Chemistry Framework (PySCF) has developed into a widely used open-source platform for electronic structure theory and quantum chemical method development. This article reviews the major advances since the previous overview in 2020, covering new modules and methodology, infrastructure changes, and performance benchmarks. △ Less

Submitted 7 April, 2026; v1 submitted 14 March, 2026; originally announced March 2026.

Fast Generation of Pipek-Mezey Wannier Functions via the Co-Iterative Augmented Hessian Method

Authors: Gengzhi Yang, Hong-Zhou Ye

Abstract: We report a $k$-point extension of the second-order co-iterative augmented Hessian (CIAH) algorithm, termed $k$-CIAH, for Pipek-Mezey (PM) localization of Wannier functions (WFs). By exploiting an efficient evaluation of the Hessian-vector product, $k$-CIAH achieves $O(N_k^2 n^3)$ scaling in both CPU time and memory, matching that of previously reported first-order $k$-space approaches while impro… ▽ More We report a $k$-point extension of the second-order co-iterative augmented Hessian (CIAH) algorithm, termed $k$-CIAH, for Pipek-Mezey (PM) localization of Wannier functions (WFs). By exploiting an efficient evaluation of the Hessian-vector product, $k$-CIAH achieves $O(N_k^2 n^3)$ scaling in both CPU time and memory, matching that of previously reported first-order $k$-space approaches while improving upon the $O(N_k^3 n^3)$ scaling of $Γ$-point CIAH, where $N_k$ denotes the number of $k$-points sampling the first Brillouin zone and $n$ characterizes the unit-cell size. Benchmark calculations on a diverse set of solids -- including insulators, semiconductors, metals, and surfaces -- demonstrate the fast and robust convergence of $k$-CIAH-based PMWF optimization, which yields an overall computational efficiency approximately 2-3--fold higher than first-order $k$-space methods and orders of magnitude higher than $Γ$-point CIAH for localizing 1000-5000 orbitals. The quality of the resulting PMWFs is further validated by accurate electronic band structures obtained via PMWF-based Wannier interpolation. △ Less

Submitted 13 May, 2026; v1 submitted 12 February, 2026; originally announced February 2026.

Ab Initio Many Body Quantum Embedding and Local Correlation in Crystalline Materials using Interpolative Separable Density Fitting

Authors: Junjie Yang, Ning Zhang, Shunyue Yuan, Jincheng Yu, Hong-Zhou Ye, Garnet Chan

Abstract: We present an efficient implementation of ab initio many-body quantum embedding and local correlation methods for infinite periodic systems through translational symmetry adapted interpolative separable density fitting, an approach which reduces the scaling of the calculations to only linear with the number of k-points. Employing this methodology, we compute correlated ground-state coupled cluster… ▽ More We present an efficient implementation of ab initio many-body quantum embedding and local correlation methods for infinite periodic systems through translational symmetry adapted interpolative separable density fitting, an approach which reduces the scaling of the calculations to only linear with the number of k-points. Employing this methodology, we compute correlated ground-state coupled cluster energies within density matrix embedding and local natural orbital correlation frameworks for both weakly and strongly correlated solids, using up to 1000 k-points. By extrapolating the local correlation domains and k-point sampling we further obtain estimates of the full coupled cluster with singles, doubles, and perturbative triples ground-state energies in the thermodynamic limit. △ Less

Submitted 22 January, 2026; originally announced January 2026.

Comments: 13 pages, 4 figures, and 4 tables

Random phase approximation-based local natural orbital coupled cluster theory

Authors: Ruiheng Song, Xiliang Gong, Aamy Bakry, Hong-Zhou Ye

Abstract: Practical applications of fragment embedding and closely related local correlation methods critically depend on a judicious choice of a low-level theory to define the local embedding subspace and to capture long-range electrostatic and correlation effects outside the embedding region. Second-order Møller-Plesset perturbation theory (MP2) is by far the most widely used correlated low-level theory;… ▽ More Practical applications of fragment embedding and closely related local correlation methods critically depend on a judicious choice of a low-level theory to define the local embedding subspace and to capture long-range electrostatic and correlation effects outside the embedding region. Second-order Møller-Plesset perturbation theory (MP2) is by far the most widely used correlated low-level theory; however, its applicability becomes questionable in systems where MP2 is known to fail either quantitatively or qualitatively. In this work, we present the random phase approximation (RPA) as a promising alternative low-level theory to MP2 within the local natural orbital-based coupled-cluster (LNO-CC) framework. We demonstrate that RPA-based LNO-CC closely matches the performance of its MP2-based counterpart for systems with sizable energy gaps, while delivering significantly faster convergence toward the canonical coupled-cluster limit for metallic systems, particularly as the thermodynamic limit is approached. These results highlight the critical role of the low-level theory in fragment embedding and local correlation methods and identify RPA as a compelling alternative to the commonly used MP2. △ Less

Submitted 13 May, 2026; v1 submitted 31 December, 2025; originally announced January 2026.

Color2Struct: efficient and accurate deep-learning inverse design of structural color with controllable inference

Authors: Sichao Shan, Han Ye, Zhengmei Yang, Junpeng Hou, Zhitong Li

Abstract: Deep learning (DL) has revolutionized many fields such as materials design and protein folding. Recent studies have demonstrated the advantages of DL in the inverse design of structural colors, by effectively learning the complex nonlinear relations between structure parameters and optical responses, as dictated by the physical laws of light. While several models, such as tandem neural networks an… ▽ More Deep learning (DL) has revolutionized many fields such as materials design and protein folding. Recent studies have demonstrated the advantages of DL in the inverse design of structural colors, by effectively learning the complex nonlinear relations between structure parameters and optical responses, as dictated by the physical laws of light. While several models, such as tandem neural networks and generative adversarial networks, have been proposed, these methods can be biased and are difficult to scale up to complex structures. Moreover, the difficulty in incorporating physical constraints at the inference time hinders the controllability of the model-predicted spectra. In this work, we propose Color2Struct, a universal framework for efficient and accurate inverse design of structural colors with controllable predictions. By utilizing sampling bias correction, adaptive loss weighting, and physics-guided inference, Color2Struct improves the prediction of tandem networks by 65% (color difference) and 48% (short-wave near-infrared reflectivity) in designing RGB primary colors. These improvements make Color2Struct highly promising for applications in high-end display technologies and solar thermal energy harvesting. In experiments, the nanostructure samples are fabricated using a standard thin-film deposition method and their reflectance spectra are measured to validate the designs. Our work provides an efficient and highly optimized method for controllable inverse design, benefiting future explorations of more intricate structures. The proposed framework can be further generalized to a wide range of fields beyond nanophotonics. △ Less

Submitted 1 October, 2025; originally announced October 2025.

Reaction dynamics of lithium-mediated electrolyte decomposition using machine learning potentials

Authors: Sohang Kundu, Diana Chamaki, Hong-Zhou Ye, Garvit Agarwal, Timothy C. Berkelbach

Abstract: We study the ring-opening decomposition of ethylene carbonate in the presence of a single lithium atom and on the surface of lithium metal. Combining accurate electronic structure theory, enhanced sampling, and machine learning, we fine-tune the MACE-MP0 foundation model and apply the resulting machine learning potentials to obtain statistically converged free energy profiles and reaction rates. W… ▽ More We study the ring-opening decomposition of ethylene carbonate in the presence of a single lithium atom and on the surface of lithium metal. Combining accurate electronic structure theory, enhanced sampling, and machine learning, we fine-tune the MACE-MP0 foundation model and apply the resulting machine learning potentials to obtain statistically converged free energy profiles and reaction rates. We confirm that the level of electronic structure theory is important, and inaccurate density functionals can overestimate the reaction rate by up to nine orders of magnitude. We also find that harmonic transition state theory underestimates reaction rates by about one order of magnitude. For the surface reaction, we find and characterize a new, ultrafast decomposition pathway wherein the carbonyl is deeply inserted into the lithium surface and bent by about 70$^\circ$. This reaction, which occurs in a few tens of picoseconds, generates a ring-opened intermediate that is a precursor for CO or CO$_2$ formation; by contrast, an alternative pathway that yields CO$_3^{2-}$ and ethylene is found to be non-competitive, occurring on a timescale of tens of nanoseconds. △ Less

Submitted 17 September, 2025; originally announced September 2025.

Comments: 7 pages, 4 figures

Unphysical Solutions in Coupled-Cluster-Based Random Phase Approximation and How to Avoid Them

Authors: Ruiheng Song, Xiliang Gong, Hong-Zhou Ye

Abstract: The direct ring coupled-cluster doubles (drCCD)-based random phase approximation (RPA) has provided an attractive framework for the development and application of RPA-related methods. However, a potential unphysical solution issue recently reported by Rekkedal and co-workers (J. Chem. Phys. 139, 081101, 2013) has raised significant concerns regarding the general applicability of coupled-cluster-ba… ▽ More The direct ring coupled-cluster doubles (drCCD)-based random phase approximation (RPA) has provided an attractive framework for the development and application of RPA-related methods. However, a potential unphysical solution issue recently reported by Rekkedal and co-workers (J. Chem. Phys. 139, 081101, 2013) has raised significant concerns regarding the general applicability of coupled-cluster-based RPA, particularly in small-gap systems where RPA is anticipated to outperform commonly employed second-order perturbation theory. In this work, we elucidate the underlying origin of the multi-solution issue in drCCD and develop both a practical criterion for validating drCCD solutions and improved preconditioners based on level shifting and regularized MP2 methods for stabilizing the iterative solution of the drCCD equation. We demonstrate the robustness and effectiveness of our approach through representative systems -- including molecules with stretched bonds, large conjugated systems, and metallic clusters -- where standard drCCD iteration encounters convergence difficulties. Furthermore, we extend our approach to various recently developed reduced-scaling drCCD-based RPA methods, thereby establishing a foundation for their stable application to large-scale problems. The extension of our approach to RPA with exchange, quasiparticle RPA, and particle-particle RPA is also discussed. △ Less

Submitted 14 August, 2025; originally announced August 2025.

Comments: 7 pages, 2 figures, 1 table

MIMOSA: Multi-parametric Imaging using Multiple-echoes with Optimized Simultaneous Acquisition for highly-efficient quantitative MRI

Authors: Yuting Chen, Yohan Jun, Amir Heydari, Xingwang Yong, Jiye Kim, Jongho Lee, Huafeng Liu, Huihui Ye, Borjan Gagoski, Shohei Fujita, Berkin Bilgic

Abstract: Purpose: To develop a new sequence, MIMOSA, for highly-efficient T1, T2, T2*, proton density (PD), and source separation quantitative susceptibility mapping (QSM). Methods: MIMOSA was developed based on 3D-quantification using an interleaved Look-Locker acquisition sequence with T2 preparation pulse (3D-QALAS) by combining 3D turbo Fast Low Angle Shot (FLASH) and multi-echo gradient echo acquisiti… ▽ More Purpose: To develop a new sequence, MIMOSA, for highly-efficient T1, T2, T2*, proton density (PD), and source separation quantitative susceptibility mapping (QSM). Methods: MIMOSA was developed based on 3D-quantification using an interleaved Look-Locker acquisition sequence with T2 preparation pulse (3D-QALAS) by combining 3D turbo Fast Low Angle Shot (FLASH) and multi-echo gradient echo acquisition modules with a spiral-like Cartesian trajectory to facilitate highly-efficient acquisition. Simulations were performed to optimize the sequence. Multi-contrast/-slice zero-shot self-supervised learning algorithm was employed for reconstruction. The accuracy of quantitative mapping was assessed by comparing MIMOSA with 3D-QALAS and reference techniques in both ISMRM/NIST phantom and in-vivo experiments. MIMOSA's acceleration capability was assessed at R = 3.3, 6.5, and 11.8 in in-vivo experiments, with repeatability assessed through scan-rescan studies. Beyond the 3T experiments, mesoscale quantitative mapping was performed at 750 um isotropic resolution at 7T. Results: Simulations demonstrated that MIMOSA achieved improved parameter estimation accuracy compared to 3D-QALAS. Phantom experiments indicated that MIMOSA exhibited better agreement with the reference techniques than 3D-QALAS. In-vivo experiments demonstrated that an acceleration factor of up to R = 11.8-fold can be achieved while preserving parameter estimation accuracy, with intra-class correlation coefficients of 0.998 (T1), 0.973 (T2), 0.947 (T2*), 0.992 (QSM), 0.987 (paramagnetic susceptibility), and 0.977 (diamagnetic susceptibility) in scan-rescan studies. Whole-brain T1, T2, T2*, PD, source separation QSM were obtained with 1 mm isotropic resolution in 3 min at 3T and 750 um isotropic resolution in 13 min at 7T. Conclusion: MIMOSA demonstrated potential for highly-efficient multi-parametric mapping. △ Less

Submitted 13 August, 2025; originally announced August 2025.

Comments: 48 pages, 21 figures, 3 tables

A compact unshielded optically-pumped magnetic gradiometer

Authors: Hangfei Ye, Chenlu Xu, Min Hu, Haifeng Dong

Abstract: Optically-pumped magnetic gradiometers (OPGs) play a crucial role in applications such as magnetic anomaly detection and bio-magnetic measurements. This study classifies current OPGs into four types based on their differential modes: voltage, frequency, optical rotation, and magnetic field differential modes. We introduce the concept of inherent Common-Mode Rejection Ratio (CMRR) and analyze the d… ▽ More Optically-pumped magnetic gradiometers (OPGs) play a crucial role in applications such as magnetic anomaly detection and bio-magnetic measurements. This study classifies current OPGs into four types based on their differential modes: voltage, frequency, optical rotation, and magnetic field differential modes. We introduce the concept of inherent Common-Mode Rejection Ratio (CMRR) and analyze the differences between the inherent CMRR and the measured CMRR, as well as the upper limit of inherent CMRR. We point out that although magnetic field differential method has the potential to increase inherent CMRR by a factor of 1+AF, the difference between the feedback gains is often neglected, which may set the limit of inherent CMRR. We designed and fabricated a compact, unshielded OPG with a specially designed scheme to minimize the distance between the sensing heads and the magnetic source. Measurement results demonstrate a measured CMRR of 1200@1Hz and a sensitivity of approximately 5 pT/cm/\sqrt{Hz} from 1 Hz to 100 Hz. △ Less

Submitted 4 March, 2025; originally announced March 2025.

Journal ref: Review of Scientific Instruments, 96, p.055004, 2025

Clinically Ready Magnetic Microrobots for Targeted Therapies

Authors: Fabian C. Landers, Lukas Hertle, Vitaly Pustovalov, Derick Sivakumaran, Oliver Brinkmann, Kirstin Meiners, Pascal Theiler, Valentin Gantenbein, Andrea Veciana, Michael Mattmann, Silas Riss, Simone Gervasoni, Christophe Chautems, Hao Ye, Semih Sevim, Andreas D. Flouris, Josep Puigmartí-Luis, Tiago Sotto Mayor, Pedro Alves, Tessa Lühmann, Xiangzhong Chen, Nicole Ochsenbein, Ueli Moehrlen, Philipp Gruber, Miriam Weisskopf , et al. (3 additional authors not shown)

Abstract: Systemic drug administration often causes off-target effects limiting the efficacy of advanced therapies. Targeted drug delivery approaches increase local drug concentrations at the diseased site while minimizing systemic drug exposure. We present a magnetically guided microrobotic drug delivery system capable of precise navigation under physiological conditions. This platform integrates a clinica… ▽ More Systemic drug administration often causes off-target effects limiting the efficacy of advanced therapies. Targeted drug delivery approaches increase local drug concentrations at the diseased site while minimizing systemic drug exposure. We present a magnetically guided microrobotic drug delivery system capable of precise navigation under physiological conditions. This platform integrates a clinical electromagnetic navigation system, a custom-designed release catheter, and a dissolvable capsule for accurate therapeutic delivery. In vitro tests showed precise navigation in human vasculature models, and in vivo experiments confirmed tracking under fluoroscopy and successful navigation in large animal models. The microrobot balances magnetic material concentration, contrast agent loading, and therapeutic drug capacity, enabling effective hosting of therapeutics despite the integration complexity of its components, offering a promising solution for precise targeted drug delivery. △ Less

Submitted 20 January, 2025; originally announced January 2025.

Dynamic tuning of ENZ wavelength in conductive polymer films via polaron excitation

Authors: Hongqi Liu, Junjun Jia, Menghui Jia, Chengcan Han, Sanjun Zhang, Hui Ye, Heping Zeng

Abstract: Traditional metal and n-type doped semiconductor materials serve as emerging epsilon-near-zero (ENZ) materials, showcasing great potential for nonlinear photonic applications. However, a significant limitation for such materials is the lack of versatile ENZ wavelength tuning, and thus dynamic tuning of the ENZ wavelength remains a technical challenge, thereby restricting their potential applicatio… ▽ More Traditional metal and n-type doped semiconductor materials serve as emerging epsilon-near-zero (ENZ) materials, showcasing great potential for nonlinear photonic applications. However, a significant limitation for such materials is the lack of versatile ENZ wavelength tuning, and thus dynamic tuning of the ENZ wavelength remains a technical challenge, thereby restricting their potential applications, such as multi-band communications. Here, dynamic tuning of the ENZ wavelength in p-type organic PEDOT: PSS films is achieved through a reversible change in hole concentrations originated from the polaron formation/decoupling following optical excitation, and a tunable ENZ wavelength shift up to 150 nm is observed. Experimental investigations about ultrafast dynamics of polaron excitation reveal an approximately 80 fs time constant for polaron buildup and an approximately 280 fs time constant for polaron decoupling, indicating the potential of reversal ultrafast switching for the ENZ wavelength within subpicosecond time scale. These findings suggest that $p$--type organic semiconductors can serve as a novel platform for dynamically tuning the ENZ wavelength through polaron excitation, opening new possibilities for ENZ--based nonlinear optical applications in flexible optoelectronics. △ Less

Submitted 25 December, 2024; originally announced December 2024.

Efficient Implementation of the Random Phase Approximation with Domain-based Local Pair Natural Orbitals

Authors: Yu Hsuan Liang, Xing Zhang, Garnet Kin-Lic Chan, Timothy C. Berkelbach, Hong-Zhou Ye

Abstract: We present an efficient implementation of the random phase approximation (RPA) for molecular systems within the domain-based local pair natural orbital (DLPNO) framework. With optimized parameters, DLPNO-RPA achieves approximately 99.9% accuracy in the total correlation energy compared to a canonical implementation, enabling highly accurate reaction energies and potential energy surfaces to be com… ▽ More We present an efficient implementation of the random phase approximation (RPA) for molecular systems within the domain-based local pair natural orbital (DLPNO) framework. With optimized parameters, DLPNO-RPA achieves approximately 99.9% accuracy in the total correlation energy compared to a canonical implementation, enabling highly accurate reaction energies and potential energy surfaces to be computed while substantially reducing computational costs. As an application, we demonstrate the capability of DLPNO-RPA to efficiently calculate basis set-converged binding energies for a set of large molecules, with results showing excellent agreement with high-level reference data from both coupled cluster and diffusion Monte Carlo. This development paves the way for the routine use of RPA-based methods in molecular quantum chemistry. △ Less

Submitted 11 November, 2024; originally announced November 2024.

Comments: 6 pages, 4 figures

Journal ref: J. Chem. Theory Comput. 2025, 21, 6, 2918--2927

Diabatic states of charge transfer with constrained charge equilibration

Authors: Sohang Kundu, Hong-Zhou Ye, Timothy C. Berkelbach

Abstract: Charge transfer (CT) processes that are electronically non-adiabatic are ubiquitous in chemistry, biology, and materials science, but their theoretical description requires diabatic states or adiabatic excited states. For complex systems, these latter states are more difficult to calculate than the adiabatic ground state. Here, we propose a simple method to obtain diabatic states, including energi… ▽ More Charge transfer (CT) processes that are electronically non-adiabatic are ubiquitous in chemistry, biology, and materials science, but their theoretical description requires diabatic states or adiabatic excited states. For complex systems, these latter states are more difficult to calculate than the adiabatic ground state. Here, we propose a simple method to obtain diabatic states, including energies and charges, by constraining the atomic charges within the charge equilibration framework. For two-state systems, the exact diabatic coupling can be determined, from which the adiabatic excited-state energy can also be calculated. The method can be viewed as an affordable alternative to constrained density functional theory (CDFT), and so we call it constrained charge equilibration (CQEq). We test the CQEq method on the anthracene-tetracyanoethylene CT complex and the reductive decomposition of ethylene carbonate on a lithium metal surface. We find that CQEq predicts diabatic energies, charges, and adiabatic excitation energies in good agreement with CDFT, and we propose that CQEq is promising for combination with machine learning force fields to study non-adiabatic CT in the condensed phase. △ Less

Submitted 7 November, 2024; originally announced November 2024.

Comments: 9 pages, 5 figures

SK-PINN: Accelerated physics-informed deep learning by smoothing kernel gradients

Authors: Cunliang Pan, Chengxuan Li, Yu Liu, Yonggang Zheng, Hongfei Ye

Abstract: The automatic differentiation (AD) in the vanilla physics-informed neural networks (PINNs) is the computational bottleneck for the high-efficiency analysis. The concept of derivative discretization in smoothed particle hydrodynamics (SPH) can provide an accelerated training method for PINNs. In this paper, smoothing kernel physics-informed neural networks (SK-PINNs) are established, which solve di… ▽ More The automatic differentiation (AD) in the vanilla physics-informed neural networks (PINNs) is the computational bottleneck for the high-efficiency analysis. The concept of derivative discretization in smoothed particle hydrodynamics (SPH) can provide an accelerated training method for PINNs. In this paper, smoothing kernel physics-informed neural networks (SK-PINNs) are established, which solve differential equations using smoothing kernel discretization. It is a robust framework capable of solving problems in the computational mechanics of complex domains. When the number of collocation points gradually increases, the training speed of SK-PINNs significantly surpasses that of vanilla PINNs. In cases involving large collocation point sets or higher-order problems, SK-PINN training can be up to tens of times faster than vanilla PINN. Additionally, analysis using neural tangent kernel (NTK) theory shows that the convergence rates of SK-PINNs are consistent with those of vanilla PINNs. The superior performance of SK-PINNs is demonstrated through various examples, including regular and complex domains, as well as forward and inverse problems in fluid dynamics and solid mechanics. △ Less

Submitted 8 November, 2024; v1 submitted 20 October, 2024; originally announced November 2024.

Periodic Local Coupled-Cluster Theory for Insulators and Metals

Authors: Hong-Zhou Ye, Timothy C. Berkelbach

Abstract: We describe the implementation details of periodic local coupled-cluster theory with single and double excitations (CCSD) and perturbative triple excitations [CCSD(T)] using local natural orbitals (LNOs) and $k$-point symmetry. We discuss and compare several choices for orbital localization, fragmentation, and LNO construction. By studying diamond and lithium, we demonstrate that periodic LNO-CC t… ▽ More We describe the implementation details of periodic local coupled-cluster theory with single and double excitations (CCSD) and perturbative triple excitations [CCSD(T)] using local natural orbitals (LNOs) and $k$-point symmetry. We discuss and compare several choices for orbital localization, fragmentation, and LNO construction. By studying diamond and lithium, we demonstrate that periodic LNO-CC theory can be applied with equal success to both insulators and metals, achieving speedups of two to three orders of magnitude even for moderately sized $k$-point meshes. Our final predictions of the equilibrium cohesive energy, lattice constant, and bulk modulus for diamond and lithium are in good agreement with previous theoretical predictions and experimental results. △ Less

Submitted 15 July, 2024; originally announced July 2024.

Comments: 11 pages, 9 figures, 2 tables

Journal ref: J. Chem. Theory Comput. 2024, 20, 20, 8948--8959

Study of the decay and production properties of $D_{s1}(2536)$ and $D_{s2}^*(2573)$

Authors: M. Ablikim, M. N. Achasov, P. Adlarson, O. Afedulidis, X. C. Ai, R. Aliberti, A. Amoroso, Q. An, Y. Bai, O. Bakina, I. Balossino, Y. Ban, H. -R. Bao, V. Batozskaya, K. Begzsuren, N. Berger, M. Berlowski, M. Bertani, D. Bettoni, F. Bianchi, E. Bianco, A. Bortone, I. Boyko, R. A. Briere, A. Brueggemann , et al. (645 additional authors not shown)

Abstract: The $e^+e^-\rightarrow D_s^+D_{s1}(2536)^-$ and $e^+e^-\rightarrow D_s^+D^*_{s2}(2573)^-$ processes are studied using data samples collected with the BESIII detector at center-of-mass energies from 4.530 to 4.946~GeV. The absolute branching fractions of $D_{s1}(2536)^- \rightarrow \bar{D}^{*0}K^-$ and $D_{s2}^*(2573)^- \rightarrow \bar{D}^0K^-$ are measured for the first time to be… ▽ More The $e^+e^-\rightarrow D_s^+D_{s1}(2536)^-$ and $e^+e^-\rightarrow D_s^+D^*_{s2}(2573)^-$ processes are studied using data samples collected with the BESIII detector at center-of-mass energies from 4.530 to 4.946~GeV. The absolute branching fractions of $D_{s1}(2536)^- \rightarrow \bar{D}^{*0}K^-$ and $D_{s2}^*(2573)^- \rightarrow \bar{D}^0K^-$ are measured for the first time to be $(35.9\pm 4.8\pm 3.5)\%$ and $(37.4\pm 3.1\pm 4.6)\%$, respectively. The measurements are in tension with predictions based on the assumption that the $D_{s1}(2536)$ and $D_{s2}^*(2573)$ are dominated by a bare $c\bar{s}$ component. The $e^+e^-\rightarrow D_s^+D_{s1}(2536)^-$ and $e^+e^-\rightarrow D_s^+D^*_{s2}(2573)^-$ cross sections are measured, and a resonant structure at around 4.6~GeV with a width of 50~MeV is observed for the first time with a statistical significance of $15σ$ in the $e^+e^-\rightarrow D_s^+D^*_{s2}(2573)^-$ process. It could be the $Y(4626)$ found by the Belle collaboration in the $D_s^+D_{s1}(2536)^{-}$ final state, since they have similar masses and widths. There is also evidence for a structure at around 4.75~GeV in both processes. △ Less

Submitted 10 July, 2024; originally announced July 2024.

Artificial Intelligence for Neuro MRI Acquisition: A Review

Authors: Hongjia Yang, Guanhua Wang, Ziyu Li, Haoxiang Li, Jialan Zheng, Yuxin Hu, Xiaozhi Cao, Congyu Liao, Huihui Ye, Qiyuan Tian

Abstract: Magnetic resonance imaging (MRI) has significantly benefited from the resurgence of artificial intelligence (AI). By leveraging AI's capabilities in large-scale optimization and pattern recognition, innovative methods are transforming the MRI acquisition workflow, including planning, sequence design, and correction of acquisition artifacts. These emerging algorithms demonstrate substantial potenti… ▽ More Magnetic resonance imaging (MRI) has significantly benefited from the resurgence of artificial intelligence (AI). By leveraging AI's capabilities in large-scale optimization and pattern recognition, innovative methods are transforming the MRI acquisition workflow, including planning, sequence design, and correction of acquisition artifacts. These emerging algorithms demonstrate substantial potential in enhancing the efficiency and throughput of acquisition steps. This review discusses several pivotal AI-based methods in neuro MRI acquisition, focusing on their technological advances, impact on clinical practice, and potential risks. △ Less

Submitted 9 June, 2024; originally announced June 2024.

Comments: Magn Reson Mater Phy (2024)

Performant Automatic Differentiation of Local Coupled Cluster Theories: Response Properties and Ab Initio Molecular Dynamics

Authors: Xing Zhang, Chenghan Li, Hong-Zhou Ye, Timothy C. Berkelbach, Garnet Kin-Lic Chan

Abstract: In this work, we introduce a differentiable implementation of the local natural orbital coupled cluster (LNOCC) method within the automatic differentiation framework of the PySCFAD package. The implementation is comprehensively tuned for enhanced performance, which enables the calculation of first-order static response properties on medium-sized molecular systems using coupled cluster theory with… ▽ More In this work, we introduce a differentiable implementation of the local natural orbital coupled cluster (LNOCC) method within the automatic differentiation framework of the PySCFAD package. The implementation is comprehensively tuned for enhanced performance, which enables the calculation of first-order static response properties on medium-sized molecular systems using coupled cluster theory with single, double, and perturbative triple excitations [CCSD(T)]. We evaluate the accuracy of our method by benchmarking it against the canonical CCSD(T) reference for nuclear gradients, dipole moments, and geometry optimizations. In addition, we demonstrate the possibility of property calculations for chemically interesting systems through the computation of bond orders and Mössbauer spectroscopy parameters for a [NiFe]-hydrogenase active site model, along with the simulation of infrared (IR) spectra via ab initio LNO-CC molecular dynamics for a protonated water hexamer. △ Less

Submitted 2 June, 2024; v1 submitted 3 April, 2024; originally announced April 2024.

Learning Robust Precipitation Forecaster by Temporal Frame Interpolation

Authors: Lu Han, Xu-Yang Chen, Han-Jia Ye, De-Chuan Zhan

Abstract: Recent advances in deep learning have significantly elevated weather prediction models. However, these models often falter in real-world scenarios due to their sensitivity to spatial-temporal shifts. This issue is particularly acute in weather forecasting, where models are prone to overfit to local and temporal variations, especially when tasked with fine-grained predictions. In this paper, we add… ▽ More Recent advances in deep learning have significantly elevated weather prediction models. However, these models often falter in real-world scenarios due to their sensitivity to spatial-temporal shifts. This issue is particularly acute in weather forecasting, where models are prone to overfit to local and temporal variations, especially when tasked with fine-grained predictions. In this paper, we address these challenges by developing a robust precipitation forecasting model that demonstrates resilience against such spatial-temporal discrepancies. We introduce Temporal Frame Interpolation (TFI), a novel technique that enhances the training dataset by generating synthetic samples through interpolating adjacent frames from satellite imagery and ground radar data, thus improving the model's robustness against frame noise. Moreover, we incorporate a unique Multi-Level Dice (ML-Dice) loss function, leveraging the ordinal nature of rainfall intensities to improve the model's performance. Our approach has led to significant improvements in forecasting precision, culminating in our model securing \textit{1st place} in the transfer learning leaderboard of the \textit{Weather4cast'23} competition. This achievement not only underscores the effectiveness of our methodologies but also establishes a new standard for deep learning applications in weather forecasting. Our code and weights have been public on \url{https://github.com/Secilia-Cxy/UNetTFI}. △ Less

Submitted 1 December, 2023; v1 submitted 30 November, 2023; originally announced November 2023.

Comments: Previous version has text overlap with last year's paper arXiv:2212.02968 since the competition's datasets does not change. We restate the dataset description to avoid it. We also polish the overall writing

On-Command Disassembly of Microrobotic Superstructures for Transport and Delivery of Magnetic Micromachines

Authors: Fabian C. Landers, Valentin Gantenbein, Lukas Hertle, Andrea Veciana, Joaquin Llacer-Wintle, Xiang-Zhong Chen, Hao Ye, Carlos Franco, Josep Puigmarti-Luis, Minsoo Kim, Bradley J. Nelson, Salvador Pane

Abstract: Magnetic microrobots have been developed for navigating microscale environments by means of remote magnetic fields. However, limited propulsion speeds at small scales remain an issue in the maneuverability of these devices as magnetic force and torque are proportional to their magnetic volume. Here, we propose a microrobotic superstructure, which, as analogous to a supramolecular system, consists… ▽ More Magnetic microrobots have been developed for navigating microscale environments by means of remote magnetic fields. However, limited propulsion speeds at small scales remain an issue in the maneuverability of these devices as magnetic force and torque are proportional to their magnetic volume. Here, we propose a microrobotic superstructure, which, as analogous to a supramolecular system, consists of two or more microrobotic units that are interconnected and organized through a physical (transient) component (a polymeric frame or a thread). Our superstructures consist of microfabricated magnetic helical micromachines interlocked by a magnetic gelatin nanocomposite containing iron oxide nanoparticles (IONPs). While the microhelices enable the motion of the superstructure, the IONPs serve as heating transducers for dissolving the gelatin chassis via magnetic hyperthermia. In a practical demonstration, we showcase the superstructure's motion with a gradient magnetic field in a large channel, the disassembly of the superstructure and release of the helical micromachines by a high-frequency alternating magnetic field, and the corkscrew locomotion of the released helices through a small channel via a rotating magnetic field. This adaptable microrobotic superstructure reacts to different magnetic inputs, which could be used to perform complex delivery procedures within intricate regions of the human body. △ Less

Submitted 28 September, 2023; originally announced October 2023.

Adsorption and Vibrational Spectroscopy of CO on the Surface of MgO from Periodic Local Coupled-Cluster Theory

Authors: Hong-Zhou Ye, Timothy C. Berkelbach

Abstract: The adsorption of CO on the surface of MgO has long been a model problem in surface chemistry. Here, we report periodic Gaussian-based calculations for this problem using second-order perturbation theory (MP2) and coupled-cluster theory with single and double excitations (CCSD) and perturbative triple excitations [CCSD(T)], with the latter two performed using a recently developed extension of the… ▽ More The adsorption of CO on the surface of MgO has long been a model problem in surface chemistry. Here, we report periodic Gaussian-based calculations for this problem using second-order perturbation theory (MP2) and coupled-cluster theory with single and double excitations (CCSD) and perturbative triple excitations [CCSD(T)], with the latter two performed using a recently developed extension of the local natural orbital approximation to problems with periodic boundary conditions. The low cost of periodic local correlation calculations allows us to calculate the full CCSD(T) binding curve of CO approaching the surface of MgO (and thus the adsorption energy) and the two-dimensional potential energy surface (PES) as a function of the distance from the surface and the CO stretching coordinate. From the PES, we obtain the fundamental vibrational frequency of CO on MgO, whose shift from the gas phase value is a common experimental probe of surface adsorption. We find that CCSD(T) correctly predicts a positive frequency shift upon adsorption of $+14.7~\textrm{cm}^{-1}$, in excellent agreement with the experimental shift of $+14.3~\textrm{cm}^{-1}$. We use our CCSD(T) results to assess the accuracy of MP2, CCSD, and several density functional theory (DFT) approximations, including exchange correlation functionals and dispersion corrections. We find that MP2 and CCSD yield reasonable binding energies and frequency shifts, whereas many DFT calculations overestimate the magnitude of the adsorption energy by $5$ -- $15$~kJ/mol and predict a negative frequency shift of about $-20~\textrm{cm}^{-1}$, which we attribute to self-interaction-induced delocalization errors that are mildly ameliorated with hybrid functionals. Our findings highlight the accuracy and computational efficiency of the periodic local correlation for the simulation of surface chemistry with accurate wavefunction methods. △ Less

Submitted 27 February, 2024; v1 submitted 26 September, 2023; originally announced September 2023.

Comments: 6 pages, 3 figures, 1 tables (excluding references and SI)

Journal ref: Faraday Discuss., 2024, 254, 628--640

Ab initio surface chemistry with chemical accuracy

Authors: Hong-Zhou Ye, Timothy C. Berkelbach

Abstract: First-principles calculations are a cornerstone of modern surface science and heterogeneous catalysis. However, accurate reaction energies and barrier heights are frequently inaccessible due to the approximations demanded by the large number of atoms. Here we combine developments in local correlation and periodic correlated wavefunction theory to solve the many-electron Schrödinger equation for mo… ▽ More First-principles calculations are a cornerstone of modern surface science and heterogeneous catalysis. However, accurate reaction energies and barrier heights are frequently inaccessible due to the approximations demanded by the large number of atoms. Here we combine developments in local correlation and periodic correlated wavefunction theory to solve the many-electron Schrödinger equation for molecules on surfaces with chemical accuracy, commonly defined as 1~kcal/mol. As a demonstration, we study water on the surface of \ce{Al2O3} and \ce{TiO2}, two prototypical and industrially important metal oxides for which we obtain converged energies at the level of coupled-cluster theory with single, double, and perturbative triple excitations [CCSD(T)], commonly known as the "gold-standard" in molecular quantum chemistry. We definitively resolve the energetics associated with water adsorption and dissociation, enabling us to address recent experiments and to analyze the errors of more commonly used approximate theories. △ Less

Submitted 7 February, 2024; v1 submitted 25 September, 2023; originally announced September 2023.

Comments: 6 pages (w/o SI), 3 figures

Toward linear scaling auxiliary field quantum Monte Carlo with local natural orbitals

Authors: Jo S. Kurian, Hong-Zhou Ye, Ankit Mahajan, Timothy C. Berkelbach, Sandeep Sharma

Abstract: We develop a local correlation variant of auxiliary field quantum Monte Carlo (AFQMC) that is based on local natural orbitals (LNO-AFQMC). In LNO-AFQMC, independent AFQMC calculations are performed for each localized occupied orbital using a truncated set of tailored orbitals. Because the size of this space does not grow with system size for a target accuracy, the method has linear scaling. Applyi… ▽ More We develop a local correlation variant of auxiliary field quantum Monte Carlo (AFQMC) that is based on local natural orbitals (LNO-AFQMC). In LNO-AFQMC, independent AFQMC calculations are performed for each localized occupied orbital using a truncated set of tailored orbitals. Because the size of this space does not grow with system size for a target accuracy, the method has linear scaling. Applying LNO AFQMC to molecular problems containing a few hundred to a thousand orbitals, we demonstrate convergence of total energies with significantly reduced costs. The savings are more significant for larger systems and larger basis sets. However, even for our smallest system studied, we find that LNO-AFQMC is cheaper than canonical AFQMC, in contrast with many other reduced-scaling methods. Perhaps most significantly, we show that energy differences converge much more quickly than total energies, making the method ideal for applications in chemistry and material science. Our work paves the way for linear scaling AFQMC calculations of strongly correlated systems, which would have a transformative effect on ab initio quantum chemistry. △ Less

Submitted 23 August, 2023; originally announced August 2023.

Comments: 9 pages, 2 figures

Can spin-component scaled MP2 achieve kJ/mol accuracy for cohesive energies of molecular crystals?

Authors: Yu Hsuan Liang, Hong-Zhou Ye, Timothy C. Berkelbach

Abstract: Achieving kJ/mol accuracy in the cohesive energy of molecular crystals, as necessary for crystal structure prediction and the resolution of polymorphism, is an ongoing challenge in computational materials science. Here, we evaluate the performance of second-order Møller-Plesset perturbation theory (MP2), including its spin-component scaled models, by calculating the cohesive energies of the 23 mol… ▽ More Achieving kJ/mol accuracy in the cohesive energy of molecular crystals, as necessary for crystal structure prediction and the resolution of polymorphism, is an ongoing challenge in computational materials science. Here, we evaluate the performance of second-order Møller-Plesset perturbation theory (MP2), including its spin-component scaled models, by calculating the cohesive energies of the 23 molecular crystals contained in the X23 dataset. Our calculations are performed with periodic boundary conditions and Brillouin zone sampling, and we converge results to the thermodynamic limit and the complete basis set limit to an accuracy of about 1 kJ/mol (0.25 kcal/mol), which is rarely achieved in previous MP2 calculations of molecular crystals. Comparing to experimental cohesive energies, we find that MP2 has a mean absolute error of 12.9 kJ/mol, which is comparable to that of DFT using the PBE functional and TS dispersion correction. Separate scaling of the opposite-spin and same-spin components of the correlation energy, with parameters previously determined for molecular interactions, reduces the mean absolute error to 9.5 kJ/mol, and reoptimizing the spin-component scaling parameters for the X23 set further reduces the mean absolute error to 7.5 kJ/mol. △ Less

Submitted 26 July, 2023; originally announced July 2023.

Forward Laplacian: A New Computational Framework for Neural Network-based Variational Monte Carlo

Authors: Ruichen Li, Haotian Ye, Du Jiang, Xuelan Wen, Chuwei Wang, Zhe Li, Xiang Li, Di He, Ji Chen, Weiluo Ren, Liwei Wang

Abstract: Neural network-based variational Monte Carlo (NN-VMC) has emerged as a promising cutting-edge technique of ab initio quantum chemistry. However, the high computational cost of existing approaches hinders their applications in realistic chemistry problems. Here, we report the development of a new NN-VMC method that achieves a remarkable speed-up by more than one order of magnitude, thereby greatly… ▽ More Neural network-based variational Monte Carlo (NN-VMC) has emerged as a promising cutting-edge technique of ab initio quantum chemistry. However, the high computational cost of existing approaches hinders their applications in realistic chemistry problems. Here, we report the development of a new NN-VMC method that achieves a remarkable speed-up by more than one order of magnitude, thereby greatly extending the applicability of NN-VMC to larger systems. Our key design is a novel computational framework named Forward Laplacian, which computes the Laplacian associated with neural networks, the bottleneck of NN-VMC, through an efficient forward propagation process. We then demonstrate that Forward Laplacian is not only versatile but also facilitates more developments of acceleration methods across various aspects, including optimization for sparse derivative matrix and efficient neural network design. Empirically, our approach enables NN-VMC to investigate a broader range of atoms, molecules and chemical reactions for the first time, providing valuable references to other ab initio methods. The results demonstrate a great potential in applying deep learning methods to solve general quantum mechanical problems. △ Less

Submitted 16 July, 2023; originally announced July 2023.

Ab initio quantum many-body description of superconducting trends in the cuprates

Authors: Zhi-Hao Cui, Junjie Yang, Johannes Tölle, Hong-Zhou Ye, Shunyue Yuan, Huanchen Zhai, Gunhee Park, Raehyun Kim, Xing Zhang, Lin Lin, Timothy C. Berkelbach, Garnet Kin-Lic Chan

Abstract: Using a systematic ab initio quantum many-body approach that goes beyond low-energy models, we directly compute the superconducting pairing order and estimate the pairing gap of several doped cuprate materials and structures within a purely electronic picture. We find that we can correctly capture two well-known trends: the pressure effect, where the pairing order and gap increase with intra-layer… ▽ More Using a systematic ab initio quantum many-body approach that goes beyond low-energy models, we directly compute the superconducting pairing order and estimate the pairing gap of several doped cuprate materials and structures within a purely electronic picture. We find that we can correctly capture two well-known trends: the pressure effect, where the pairing order and gap increase with intra-layer pressure, and the layer effect, where the pairing order and gap vary with the number of copper-oxygen layers. From these calculations, we observe that the strength of superexchange and the covalency at optimal doping are the best descriptors for these trends. Our microscopic analysis further identifies that strong short-range spin fluctuations and multi-orbital charge fluctuations drive the development of the pairing order. Our work illustrates the possibility of a material-specific ab initio understanding of unconventional high-temperature superconducting materials. △ Less

Submitted 27 January, 2025; v1 submitted 28 June, 2023; originally announced June 2023.

Comments: 11 pages, 5 figures, with supplementary materials

Journal ref: Nat Commun 16, 1845 (2025)

Electromotive force and magnetization process of a superconducting traveling-wave flux pump

Authors: Wei Wang, Jiafu Wei, Chenghuai Wu, Guangtong Ma, Hong Li, Hanxin Ye, Yuntian Zhang

Abstract: Understanding and controlling the motion of superconducting vortices has been a key issue in condensed matter physics and applied superconductivity. Here we present a method for macroscopically manipulating the vortices based on travelling wave flux pump to accurately output industrial-scale DC current into high-temperature superconducting (HTS) magnets. DC magnetic fields are used to adjust the p… ▽ More Understanding and controlling the motion of superconducting vortices has been a key issue in condensed matter physics and applied superconductivity. Here we present a method for macroscopically manipulating the vortices based on travelling wave flux pump to accurately output industrial-scale DC current into high-temperature superconducting (HTS) magnets. DC magnetic fields are used to adjust the polarity of the vortices and thus modulate the direction of the output current, which demonstrates that the DC current of the flux pump originates from the motional electromotive force ( e.m.f. ) other than the induced e.m.f.. In addition, applying different strengths of DC fields can modulate the magnitude of the output current. Further numerical simulation suggests how the flux inside the superconducting tape is controlled by different applied fields. We build a controlled flux flow model to correctly explain the behavior of vortices controlled by the flux pump, and how the motional e.m.f. is created by manipulating the vortices. Based on the method, we achieve high precision regulation of output current using adaptive control of the DC magnetic field, allowing the flux pump to output DC current just as accurate as a typical commercial power supply. This work advances the technic for macroscopic manipulation of vortices. △ Less

Submitted 4 June, 2023; originally announced June 2023.

Comments: 12 pages, 8 figures

Threshold Current for Field-free Switching of the In-plane Magnetization in the Three-terminal Magnetic Tunnel Junction

Authors: Hongjie Ye, Zhaohao Wang

Abstract: Three-terminal magnetic tunnel junction (MTJ), where non-volatile magnetization state can be switched via spin orbit torque (SOT), is attracting massive research interests since it is featured by high speed, low power, nearly unlimited endurance, etc. The threshold switching current is a key parameter for MTJ as it determines the energy efficiency. Here, with the Routh-Hurwitz criterion, we theore… ▽ More Three-terminal magnetic tunnel junction (MTJ), where non-volatile magnetization state can be switched via spin orbit torque (SOT), is attracting massive research interests since it is featured by high speed, low power, nearly unlimited endurance, etc. The threshold switching current is a key parameter for MTJ as it determines the energy efficiency. Here, with the Routh-Hurwitz criterion, we theoretically derive the threshold current for switching in-plane magnetization in the three-terminal MTJ. Two devices with field-free switching mode are investigated. The one is the Type-x device switched by the combination of SOT and spin transfer torque (STT). The other is the three-terminal MTJ with a canted easy-axis. To the best of our knowledge, this is the first theoretical work on the threshold switching current for these two devices. Our developed theoretical method shows clear physical picture, meanwhile good agreement between theoretical derivation and numerical simulation is achieved. △ Less

Submitted 5 May, 2023; originally announced May 2023.

Journal ref: SCIENCE CHINA Information Sciences, 2025

DeePMD-kit v2: A software package for Deep Potential models

Authors: Jinzhe Zeng, Duo Zhang, Denghui Lu, Pinghui Mo, Zeyu Li, Yixiao Chen, Marián Rynik, Li'ang Huang, Ziyao Li, Shaochen Shi, Yingze Wang, Haotian Ye, Ping Tuo, Jiabin Yang, Ye Ding, Yifan Li, Davide Tisi, Qiyu Zeng, Han Bao, Yu Xia, Jiameng Huang, Koki Muraoka, Yibo Wang, Junhan Chang, Fengbo Yuan , et al. (22 additional authors not shown)

Abstract: DeePMD-kit is a powerful open-source software package that facilitates molecular dynamics simulations using machine learning potentials (MLP) known as Deep Potential (DP) models. This package, which was released in 2017, has been widely used in the fields of physics, chemistry, biology, and material science for studying atomistic systems. The current version of DeePMD-kit offers numerous advanced… ▽ More DeePMD-kit is a powerful open-source software package that facilitates molecular dynamics simulations using machine learning potentials (MLP) known as Deep Potential (DP) models. This package, which was released in 2017, has been widely used in the fields of physics, chemistry, biology, and material science for studying atomistic systems. The current version of DeePMD-kit offers numerous advanced features such as DeepPot-SE, attention-based and hybrid descriptors, the ability to fit tensile properties, type embedding, model deviation, Deep Potential - Range Correction (DPRc), Deep Potential Long Range (DPLR), GPU support for customized operators, model compression, non-von Neumann molecular dynamics (NVNMD), and improved usability, including documentation, compiled binary packages, graphical user interfaces (GUI), and application programming interfaces (API). This article presents an overview of the current major version of the DeePMD-kit package, highlighting its features and technical details. Additionally, the article benchmarks the accuracy and efficiency of different models and discusses ongoing developments. △ Less

Submitted 18 April, 2023; originally announced April 2023.

Comments: 51 pages, 2 figures

ACM Class: J.2

Journal ref: J. Chem. Phys. 159, 054801 (2023)

Machine learning potentials from transfer learning of periodic correlated electronic structure methods: Application to liquid water with AFQMC, CCSD, and CCSD(T)

Authors: Michael S. Chen, Joonho Lee, Hong-Zhou Ye, Timothy C. Berkelbach, David R. Reichman, Thomas E. Markland

Abstract: Obtaining the atomistic structure and dynamics of disordered condensed phase systems from first principles remains one of the forefront challenges of chemical theory. Here we exploit recent advances in periodic electronic structure to show that, by leveraging transfer learning starting from lower tier electronic structure methods, one can obtain machine learned potential energy surfaces for liquid… ▽ More Obtaining the atomistic structure and dynamics of disordered condensed phase systems from first principles remains one of the forefront challenges of chemical theory. Here we exploit recent advances in periodic electronic structure to show that, by leveraging transfer learning starting from lower tier electronic structure methods, one can obtain machine learned potential energy surfaces for liquid water from the higher tier AFQMC, CCSD, and CCSD(T) approaches using $\le$200 energies. By performing both classical and path integral molecular dynamics simulations on these machine learned potential energy surfaces we uncover the interplay of dynamical electron correlation and nuclear quantum effects across the entire liquid range of water while providing a general strategy for efficiently utilizing periodic correlated electronic structure methods to explore disordered condensed phase systems. △ Less

Submitted 29 November, 2022; originally announced November 2022.

Polarization effects on fluorescence emission of zebrafish neurons using light-sheet microscopy

Authors: Hong Ye, Xin Xu, Jixiang Wang, Jing Wang, Yi He, Yu Mu, Guohua Shi

Abstract: Light-sheet fluorescence microscopy (LSFM) makes use of a thin plane of light to optically section and image transparent tissues or organisms {\it{in vivo}}, which has the advantages of fast imaging speed and low phototoxicity. In this paper, we have employed light-sheet microscopy to investigate the polarization effects on fluorescence emission of zebrafish neurons via modifying the electric osci… ▽ More Light-sheet fluorescence microscopy (LSFM) makes use of a thin plane of light to optically section and image transparent tissues or organisms {\it{in vivo}}, which has the advantages of fast imaging speed and low phototoxicity. In this paper, we have employed light-sheet microscopy to investigate the polarization effects on fluorescence emission of zebrafish neurons via modifying the electric oscillation orientation of the excitation light. The intensity of the fluorescence emission from the excited zebrafish larvae follows a cosine square function with respect to the polarization state of the excitation light and reveals a 40$\%$ higher fluorescence emission when the polarization orientation is orthogonal to the illumination and detection axes. Through registration and subtraction of fluorescence images under different polarization states, we have demonstrated that most of the enhanced fluorescence signals are from the nerve cells rather than the extracellular substance. This provides us a way to distinguish the cell boundaries and observe the organism structures with improved contrast and resolution. △ Less

Submitted 8 September, 2022; originally announced September 2022.

Accurate thermochemistry of covalent and ionic solids from spin-component-scaled MP2

Authors: Tamar Goldzak, Xiao Wang, Hong-Zhou Ye, Timothy C. Berkelbach

Abstract: We study the performance of spin-component-scaled second-order Møller-Plesset perturbation theory (SCS-MP2) for the prediction of the lattice constant, bulk modulus, and cohesive energy of 12 simple, three-dimensional, covalent and ionic semiconductors and insulators. We find that SCS-MP2 and the simpler scaled opposite-spin MP2 (SOS-MP2) yield predictions that are significantly improved over the… ▽ More We study the performance of spin-component-scaled second-order Møller-Plesset perturbation theory (SCS-MP2) for the prediction of the lattice constant, bulk modulus, and cohesive energy of 12 simple, three-dimensional, covalent and ionic semiconductors and insulators. We find that SCS-MP2 and the simpler scaled opposite-spin MP2 (SOS-MP2) yield predictions that are significantly improved over the already good performance of MP2. Specifically, when compared to experimental values with zero-point vibrational corrections, SCS-MP2 (SOS-MP2) yields mean absolute errors of 0.015 (0.017) Å for the lattice constant, 3.8 (3.7) GPa for the bulk modulus, and 0.06 (0.08) eV for the cohesive energy, which are smaller than those of leading density functionals by about a factor of two or more. We consider a reparameterization of the spin scaling parameters and find that the optimal parameters for these solids are very similar to those already in common use in molecular quantum chemistry, suggesting good transferability and reliable future applications to surface chemistry on insulators. △ Less

Submitted 9 August, 2022; originally announced August 2022.

Comments: 7 pages, 4 figures

Integral-direct Hartree-Fock and Møller-Plesset Perturbation Theory for Periodic Systems with Density Fitting: Application to the Benzene Crystal

Authors: Sylvia J. Bintrim, Timothy C. Berkelbach, Hong-Zhou Ye

Abstract: We present an algorithm and implementation of integral-direct, density-fitted Hartree-Fock (HF) and second-order Møller-Plesset perturbation theory (MP2) for periodic systems. The new code eliminates the formerly prohibitive storage requirements and allows us to study systems one order of magnitude larger than before at the periodic MP2 level. We demonstrate the significance of the development by… ▽ More We present an algorithm and implementation of integral-direct, density-fitted Hartree-Fock (HF) and second-order Møller-Plesset perturbation theory (MP2) for periodic systems. The new code eliminates the formerly prohibitive storage requirements and allows us to study systems one order of magnitude larger than before at the periodic MP2 level. We demonstrate the significance of the development by studying the benzene crystal in both the thermodynamic limit and the complete basis set limit, for which we predict an MP2 cohesive energy of $-72.8$ kJ/mol, which is about $10$--$15$ kJ/mol larger in magnitude than all previously reported MP2 calculations. Compared to the best theoretical estimate from literature, several modified MP2 models approach chemical accuracy in the predicted cohesive energy of the benzene crystal and hence may be promising cost-effective choices for future applications on molecular crystals. △ Less

Submitted 2 August, 2022; v1 submitted 3 June, 2022; originally announced June 2022.

Comments: 5 pages (w/o refs or SI), 3 figures, 1 table

Journal ref: J. Chem. Theory Comput. 2022, 18, 9, 5374--5381

Organic metallic epsilon-near-zero materials with large ultrafast optical nonlinearity

Authors: Qili Hu, Xinlan Yu, Hongqi Liu, Jiahuan Qiu, Wei Tang, Sen Liang, Linjun Li, Miao Du, Junjun Jia, Hui Ye

Abstract: Epsilon-near-zero (ENZ) materials have shown significant potential for nonlinear optical applications due to their ultrafast hot carriers and consequent optical nonlinearity enhancement. Modified poly(3,4-ethylenedioxythiophene) (PEDOT) films show metallic characteristics and a resultant ENZ wavelength near 1550nm through polar solvent treatment and annealing. The metallic PEDOT film exhibits an i… ▽ More Epsilon-near-zero (ENZ) materials have shown significant potential for nonlinear optical applications due to their ultrafast hot carriers and consequent optical nonlinearity enhancement. Modified poly(3,4-ethylenedioxythiophene) (PEDOT) films show metallic characteristics and a resultant ENZ wavelength near 1550nm through polar solvent treatment and annealing. The metallic PEDOT film exhibits an intrinsic optical nonlinear response that is comparable to gold and 100-fold higher than typical inorganic semiconductor ENZ materials due to π-conjugated delocalized electrons. Hot carriers generate a 22-fold increase in the optical nonlinearity coefficient of metallic PEDOT films at 1550 nm. Hot holes in metallic PEDOT films have a smaller enhancement multiple of carrier temperature and a longer relaxation time than hot electrons in inorganic ENZ materials due to the larger imaginary permittivity and hot-phonon bottleneck for carrier cooling. Our findings suggest that π-conjugated ENZ polymer may have unique ultrafast and nonlinear optical properties compared to inorganic ENZ materials, enabling new possibilities in on-chip nanophotonic devices, nonlinear optics, and plasmonics. △ Less

Submitted 5 October, 2022; v1 submitted 12 April, 2022; originally announced April 2022.

Comments: There is a mistake in our simulation part of two-temperature model. Thus we revise the parameter and reconstruct our idea. The new content is very different to this old one. Thus, we want to withdraw this paper and submit another new one

Ground-state properties of metallic solids from ab initio coupled-cluster theory

Authors: Verena A. Neufeld, Hong-Zhou Ye, Timothy C. Berkelbach

Abstract: Metallic solids are a challenging target for wavefunction-based electronic structure theories and have not been studied in great detail by such methods. Here, we use coupled-cluster theory with single and double excitations (CCSD) to study the structure of solid lithium and aluminum using optimized Gaussian basis sets. We calculate the equilibrium lattice constant, bulk modulus, and cohesive energ… ▽ More Metallic solids are a challenging target for wavefunction-based electronic structure theories and have not been studied in great detail by such methods. Here, we use coupled-cluster theory with single and double excitations (CCSD) to study the structure of solid lithium and aluminum using optimized Gaussian basis sets. We calculate the equilibrium lattice constant, bulk modulus, and cohesive energy and compare them to experimental values, finding accuracy comparable to common density functionals. Because the quantum chemical "gold standard" CCSD(T) (CCSD with perturbative triple excitations) is inapplicable to metals in the thermodynamic limit, we test two approximate improvements to CCSD, which are found to improve the predicted cohesive energies. △ Less

Submitted 4 April, 2022; originally announced April 2022.

Low-dose CT reconstruction by self-supervised learning in the projection domain

Authors: Long Zhou, Xiaozhuang Wang, Min Hou, Ping Li, Chunlong Fu, Yanjun Ren, Tingting Shao, Xi Hu, Jihong Sun, Hongwei Ye

Abstract: In the intention of minimizing excessive X-ray radiation administration to patients, low-dose computed tomography (LDCT) has become a distinct trend in radiology. However, while lowering the radiation dose reduces the risk to the patient, it also increases noise and artifacts, compromising image quality and clinical diagnosis. In most supervised learning methods, paired CT images are required, but… ▽ More In the intention of minimizing excessive X-ray radiation administration to patients, low-dose computed tomography (LDCT) has become a distinct trend in radiology. However, while lowering the radiation dose reduces the risk to the patient, it also increases noise and artifacts, compromising image quality and clinical diagnosis. In most supervised learning methods, paired CT images are required, but such images are unlikely to be available in the clinic. We present a self-supervised learning model (Noise2Projection) that fully exploits the raw projection images to reduce noise and improve the quality of reconstructed LDCT images. Unlike existing self-supervised algorithms, the proposed method only requires noisy CT projection images and reduces noise by exploiting the correlation between nearby projection images. We trained and tested the model using clinical data and the quantitative and qualitative results suggest that our model can effectively reduce LDCT image noise while also drastically removing artifacts in LDCT images. △ Less

Submitted 13 March, 2022; originally announced March 2022.

Diabatic valence-hole states in the C$_2$ molecule: "Putting Humpty Dumpty together again"

Authors: Jun Jiang, Hong-Zhou Ye, Klaas Nauta, Troy Van Voorhis, Timothy W. Schmidt, Robert W. Field

Abstract: Despite the long history of spectroscopic studies of the C$_2$ molecule, fundamental questions about its chemical bonding are still being hotly debated. The complex electronic structure of C$_2$ is a consequence of its dense manifold of near-degenerate, low-lying electronic states. A global multi-state diabatic model is proposed here to disentangle the numerous configuration interactions within fo… ▽ More Despite the long history of spectroscopic studies of the C$_2$ molecule, fundamental questions about its chemical bonding are still being hotly debated. The complex electronic structure of C$_2$ is a consequence of its dense manifold of near-degenerate, low-lying electronic states. A global multi-state diabatic model is proposed here to disentangle the numerous configuration interactions within four symmetry manifolds of C$_2$ ($^{1}Π_g$, $^{3}Π_g$, $^{1}Σ_u^+$, and $^{3}Σ_u^+$). The key concept of our model is the existence of two "valence-hole" configurations, $2σ_g^22σ_u^11π_{u}^33σ_g^2$ for $^{1,3}Π_g$ states and $2σ_g^22σ_u^11π_{u}^43σ_g^1$ for $^{1,3}Σ_u^+$ states that derive from $3σ_g\leftarrow2σ_u$ electron promotion. The lowest-energy state from each of the four C$_2$ symmetry species is dominated by this type of valence-hole configuration at its equilibrium internuclear separation. As a result of their large binding energy (nominal bond order of 3) and correlation with the 2s$^2$2p$^2$+2s2p$^3$ separated-atom configurations, the presence of these valence-hole configurations has a profound impact on the $global$ electronic structure and unimolecular dynamics of C$_2$. △ Less

Submitted 7 March, 2022; originally announced March 2022.

Observation of Gigahertz Topological Valley Hall Effect in Nanoelectromechanical Phononic Crystals

Authors: Qicheng Zhang, Daehun Lee, Lu Zheng, Xuejian Ma, Shawn I. Meyer, Li He, Han Ye, Ze Gong, Bo Zhen, Keji Lai, A. T. Charlie Johnson

Abstract: Topological phononics offers numerous opportunities in manipulating elastic waves that can propagate in solids without being backscattered. Due to the lack of nanoscale imaging tools that aid the system design, however, acoustic topological metamaterials have been mostly demonstrated in macroscale systems operating at low (kilohertz to megahertz) frequencies. Here, we report the realization of gig… ▽ More Topological phononics offers numerous opportunities in manipulating elastic waves that can propagate in solids without being backscattered. Due to the lack of nanoscale imaging tools that aid the system design, however, acoustic topological metamaterials have been mostly demonstrated in macroscale systems operating at low (kilohertz to megahertz) frequencies. Here, we report the realization of gigahertz topological valley Hall effect in nanoelectromechanical AlN membranes. Propagation of elastic wave through phononic crystals is directly visualized by microwave microscopy with unprecedented sensitivity and spatial resolution. The valley Hall edge states, protected by band topology, are vividly seen in both real- and momentum-space. The robust valley-polarized transport is evident from the wave transmission across local disorder and around sharp corners, as well as the power distribution into multiple edge channels. Our work paves the way to exploit topological physics in integrated acousto-electronic systems for classical and quantum information processing in the microwave regime. △ Less

Submitted 17 March, 2022; v1 submitted 4 February, 2022; originally announced February 2022.

Comments: 17 pages, 5 figures

Journal ref: Nature Electronics 5 (3), 157-163, 2022

Ultra-compact Si/In$_2$O$_3$ hybrid plasmonic waveguide modulator with a high bandwidth beyond 40 GHz

Authors: Yishu Huang, Jun Zheng, Bingcheng Pan, Lijia Song, Guanan Chen, Zejie Yu, Hui Ye, Daoxin Dai

Abstract: Optical modulators are required to have high modulation bandwidths and a compact footprint. In this paper we experimentally demonstrate a novel Si/In$_2$O$_3$ hybrid plasmonic waveguide modulator, which is realized by an asymmetric directional coupler (ADC) consisting of a silicon photonic waveguide and a Si/In$_2$O$_3$ hybrid plasmonic waveguide. The optical signal is modulated by radio-frequency… ▽ More Optical modulators are required to have high modulation bandwidths and a compact footprint. In this paper we experimentally demonstrate a novel Si/In$_2$O$_3$ hybrid plasmonic waveguide modulator, which is realized by an asymmetric directional coupler (ADC) consisting of a silicon photonic waveguide and a Si/In$_2$O$_3$ hybrid plasmonic waveguide. The optical signal is modulated by radio-frequency (RF) signal applied on the Au electrodes at the top of MOS capacitor and contacting the In$_2$O$_3$ thin film. The record-high modulation bandwidth of >40 GHz is realized by a silicon-doping-free metal-oxide-In$_2$O$_3$ capacitor integrated in a 3.5-$μ$m-long asymmetric directional coupler (ADC) for the first time. △ Less

Submitted 17 January, 2022; originally announced January 2022.

An FPGA Based energy correction method for one-to-one coupled PET detector: model and evaluation

Authors: Cong Ma, Xiaokun Zhao, Size Gao, Fengping Zhang, Guocheng Wu, Xing Li, Lei Lu, Hongwei Ye, Hua Qian

Abstract: A PET scanner based on silicon photomultipliers (SiPMs) has been widely used as an advanced nuclear medicine imaging technique that yields quantitative images of regional in vivo biology and biochemistry. The compact size of the SiPM allows direct one to one coupling between the scintillation crystal and the photosensor, yielding better timing and energy resolutions than the light sharing methods… ▽ More A PET scanner based on silicon photomultipliers (SiPMs) has been widely used as an advanced nuclear medicine imaging technique that yields quantitative images of regional in vivo biology and biochemistry. The compact size of the SiPM allows direct one to one coupling between the scintillation crystal and the photosensor, yielding better timing and energy resolutions than the light sharing methods that have to be used in photomultiplier tube (PMT) PET systems. To decrease the volume of readout electronics, a front end multiplexer with position decoder is a common choice for the one to one system without a highly integrated application specific integrated circuit (ASIC). However, in this case we cannot measure each crystal's deposited energy inspired by an annihilation photon, so the inter-crystal scatter (ICS) events will lead to the crystal mispositioning and then deteriorate the detector intrinsic resolution. Besides, considering the events rejection within the energy window resulting from the gain dispersion and nonlinear outputs of the SiPMs, an energy correction mechanism is needed. Yet, lack of the information of each crystal's energy will introduce large energy correction error for the ICS events. For this issue, an online energy correction mechanism implemented on a Kintext-7 Field Programmable Gate Array (FPGA) device is presented in this paper. Experiments in the laboratory were performed using an 8 x 8 segmented LYSO crystals coupled with an 8 x 8 SiPM (J-series, from ON Semiconductor) array which is under 22Na point source excitation. Test results indicate that both the energy of the non-ICS and ICS events can be precisely corrected and the energy resolution is better than 12 %. We also applied this method to an actual clinical PET scanner under a 68Ge line source to verify its multi-channel reliability. △ Less

Submitted 6 January, 2022; originally announced January 2022.

Comments: 13 pages, 12 figures,1 table,accepted by the journal of instrumentation

Correlation-consistent Gaussian basis sets for solids made simple

Authors: Hong-Zhou Ye, Timothy C. Berkelbach

Abstract: The rapidly growing interest in simulating condensed-phase materials using quantum chemistry methods calls for a library of high-quality Gaussian basis sets suitable for periodic calculations. Unfortunately, most standard Gaussian basis sets commonly used in molecular simulation show significant linear dependencies when used in close-packed solids, leading to severe numerical issues that hamper th… ▽ More The rapidly growing interest in simulating condensed-phase materials using quantum chemistry methods calls for a library of high-quality Gaussian basis sets suitable for periodic calculations. Unfortunately, most standard Gaussian basis sets commonly used in molecular simulation show significant linear dependencies when used in close-packed solids, leading to severe numerical issues that hamper the convergence to the complete basis set (CBS) limit, especially in correlated calculations. In this work, we revisit Dunning's strategy for construction of correlation-consistent basis sets and examine the relationship between accuracy and numerical stability in periodic settings. Specifically, we find that limiting the number of primitive functions avoids the appearance of problematic small exponents while still providing smooth convergence to the CBS limit. As an example, we generate double-, triple-, and quadruple-zeta correlation-consistent Gaussian basis sets for periodic calculations with Goedecker-Teter-Hutter (GTH) pseudopotentials. Our basis sets cover the main-group elements from the first three rows of the periodic table. Especially for atoms on the left side of the periodic table, our basis sets are less diffuse than those used in molecular calculations. We verify the fast and reliable convergence to the CBS limit in both Hartree-Fock and post-Hartree-Fock (MP2) calculations, using a diverse test set of $19$ semiconductors and insulators. △ Less

Submitted 3 February, 2022; v1 submitted 10 December, 2021; originally announced December 2021.

Comments: 10 pages w/o references, 7 figures, 2 tables

Journal ref: J. Chem. Theory Comput. 2022, 18, 3, 1595-1606

Accurate parameter estimation using scan-specific unsupervised deep learning for relaxometry and MR fingerprinting

Authors: Mengze Gao, Huihui Ye, Tae Hyung Kim, Zijing Zhang, Seohee So, Berkin Bilgic

Abstract: We propose an unsupervised convolutional neural network (CNN) for relaxation parameter estimation. This network incorporates signal relaxation and Bloch simulations while taking advantage of residual learning and spatial relations across neighboring voxels. Quantification accuracy and robustness to noise is shown to be significantly improved compared to standard parameter estimation methods in num… ▽ More We propose an unsupervised convolutional neural network (CNN) for relaxation parameter estimation. This network incorporates signal relaxation and Bloch simulations while taking advantage of residual learning and spatial relations across neighboring voxels. Quantification accuracy and robustness to noise is shown to be significantly improved compared to standard parameter estimation methods in numerical simulations and in vivo data for multi-echo T2 and T2* mapping. The combination of the proposed network with subspace modeling and MR fingerprinting (MRF) from highly undersampled data permits high quality T1 and T2 mapping. △ Less

Submitted 12 December, 2021; v1 submitted 7 December, 2021; originally announced December 2021.

Comments: 7 pages, 5 figures, submitted to International Society for Magnetic Resonance in Medicine 2022

Applications of Traveling Salesman Problem on the Optimal Sightseeing Orders of Macao World Heritage Sites with Real Time or Distance Values Between Every Pair of Sites

Authors: Kin Neng Tong, Iat In Fong, In Iat Li, Chi Him Anthony Cheng, Soi Chak Choi, Hau Xiang Ye, Wei Shan Lee

Abstract: The optimal route of sightseeing orders for visiting every Macao World Heritage Site at exactly once was calculated with Simulated Annealing and Metropolis Algorithm(SAMA) after considering real required time or traveling distance between pairs of sites by either driving a car, taking a bus, or on foot. We found out that, with the optimal tour path, it took roughly 78 minutes for driving a car, 11… ▽ More The optimal route of sightseeing orders for visiting every Macao World Heritage Site at exactly once was calculated with Simulated Annealing and Metropolis Algorithm(SAMA) after considering real required time or traveling distance between pairs of sites by either driving a car, taking a bus, or on foot. We found out that, with the optimal tour path, it took roughly 78 minutes for driving a car, 115 minutes on foot, while 117 minutes for taking a bus. On the other hand, the optimal total distance for driving a car would be 13.918 km while for pedestrians to walk, 7.844 km. These results probably mean that there is large space for the improvement on public transportation in this city. Comparison of computation time demanded between the brute-force enumeration of all possible paths and SAMA was also presented, together with animation of the processes for the algorithm to find out the optimal route. It is expected that computation time is astronomically increasing for the brute-force enumeration with more number of sites, while it only takes SAMA much less order of magnitude in time to calculate the optimal solution for larger number of sites. Several optimal options of routes were also provided in each transportation method. However, it is possible that in some types of transportation there could be only one optimal route having no circular or mirrored duplicates. △ Less

Submitted 29 August, 2021; originally announced September 2021.

Comments: 17 pages, 4 figures

BUDA-SAGE with self-supervised denoising enables fast, distortion-free, high-resolution T2, T2*, para- and dia-magnetic susceptibility mapping

Authors: Zijing Zhang, Long Wang, Jaejin Cho, Congyu Liao, Hyeong-Geol Shin, Xiaozhi Cao, Jongho Lee, Jinmin Xu, Tao Zhang, Huihui Ye, Kawin Setsompop, Huafeng Liu, Berkin Bilgic

Abstract: To rapidly obtain high resolution T2, T2* and quantitative susceptibility mapping (QSM) source separation maps with whole-brain coverage and high geometric fidelity. We propose Blip Up-Down Acquisition for Spin And Gradient Echo imaging (BUDA-SAGE), an efficient echo-planar imaging (EPI) sequence for quantitative mapping. The acquisition includes multiple T2*-, T2'- and T2-weighted contrasts. We a… ▽ More To rapidly obtain high resolution T2, T2* and quantitative susceptibility mapping (QSM) source separation maps with whole-brain coverage and high geometric fidelity. We propose Blip Up-Down Acquisition for Spin And Gradient Echo imaging (BUDA-SAGE), an efficient echo-planar imaging (EPI) sequence for quantitative mapping. The acquisition includes multiple T2*-, T2'- and T2-weighted contrasts. We alternate the phase-encoding polarities across the interleaved shots in this multi-shot navigator-free acquisition. A field map estimated from interim reconstructions was incorporated into the joint multi-shot EPI reconstruction with a structured low rank constraint to eliminate geometric distortion. A self-supervised MR-Self2Self (MR-S2S) neural network (NN) was utilized to perform denoising after BUDA reconstruction to boost SNR. Employing Slider encoding allowed us to reach 1 mm isotropic resolution by performing super-resolution reconstruction on BUDA-SAGE volumes acquired with 2 mm slice thickness. Quantitative T2 and T2* maps were obtained using Bloch dictionary matching on the reconstructed echoes. QSM was estimated using nonlinear dipole inversion (NDI) on the gradient echoes. Starting from the estimated R2 and R2* maps, R2' information was derived and used in source separation QSM reconstruction, which provided additional para- and dia-magnetic susceptibility maps. In vivo results demonstrate the ability of BUDA-SAGE to provide whole-brain, distortion-free, high-resolution multi-contrast images and quantitative T2 and T2* maps, as well as yielding para- and dia-magnetic susceptibility maps. Derived quantitative maps showed comparable values to conventional mapping methods in phantom and in vivo measurements. BUDA-SAGE acquisition with self-supervised denoising and Slider encoding enabled rapid, distortion-free, whole-brain T2, T2* mapping at 1 mm3 isotropic resolution in 90 seconds. △ Less

Submitted 9 September, 2021; v1 submitted 28 August, 2021; originally announced August 2021.

Tight distance-dependent estimators for screening two-center and three-center short-range Coulomb integrals over Gaussian basis functions

Authors: Hong-Zhou Ye, Timothy C. Berkelbach

Abstract: We derive distance-dependent estimators for two-center and three-center electron repulsion integrals over a short-range Coulomb potential, $\textrm{erfc}(ωr_{12})/r_{12}$. These estimators are much tighter than one based on the Schwarz inequality and can be viewed as a complement to the distance-dependent estimators for four-center short-range Coulomb integrals and for two-center and three-center… ▽ More We derive distance-dependent estimators for two-center and three-center electron repulsion integrals over a short-range Coulomb potential, $\textrm{erfc}(ωr_{12})/r_{12}$. These estimators are much tighter than one based on the Schwarz inequality and can be viewed as a complement to the distance-dependent estimators for four-center short-range Coulomb integrals and for two-center and three-center full Coulomb integrals previously reported. Because the short-range Coulomb potential is commonly used in solid-state calculations, including those with the HSE functional and with our recently introduced range-separated periodic Gaussian density fitting, we test our estimators on a diverse set of periodic systems using a wide range of the range-separation parameter $ω$. These tests demonstrate the robust tightness of our estimators, which are then used with integral screening to calculate periodic three-center short-range Coulomb integrals with linear scaling in system size. △ Less

Submitted 20 July, 2021; originally announced July 2021.

Comments: 13 pages, 8 figures (main text)

Fast FPGA algorithm for neutron-gamma discrimination

Authors: Haoqi Ye, Ge Jin, Lian Chen

Abstract: Various pulse shape discrimination methods have been used to solve the neutron-gamma discrimination problem. But most of them are limited to off-line calculation due to the computation amount and FPGA performance. In order to realize real time discriminating neutron and gamma, a new algorithm based on the traditional pulse shape discrimination methods was proposed in this paper. The new algorithm… ▽ More Various pulse shape discrimination methods have been used to solve the neutron-gamma discrimination problem. But most of them are limited to off-line calculation due to the computation amount and FPGA performance. In order to realize real time discriminating neutron and gamma, a new algorithm based on the traditional pulse shape discrimination methods was proposed in this paper. The new algorithm takes into account the physical properties of the pulse signal, which greatly reduces the computation and dead time without losing the precision, and can work on FPGA directly. It has a good performance in the actual experiment based on CLLB scintillation detector. △ Less

Submitted 9 April, 2021; originally announced April 2021.