Showing 1–50 of 123 results for author: Strachan, A

Microstructure-Aware Deep Learning Bridges Atomistics to Macroscale for Shock-to-Detonation Prediction

Authors: Simon Gonzalez-Zapata, Aidan Pantoya, Chunyu Li, Marisol Koslowski, Alejandro Strachan

Abstract: The shock-to-detonation transition in energetic materials is governed by coupled processes spanning Angstroms to millimeters and femtoseconds to microseconds, where traditional multiscale models fail due to the lack of scale separation. We address this grand challenge by directly bridging large-scale molecular dynamics (MD) simulations with continuum finite-element (FE) models using MISTnetX, a co… ▽ More The shock-to-detonation transition in energetic materials is governed by coupled processes spanning Angstroms to millimeters and femtoseconds to microseconds, where traditional multiscale models fail due to the lack of scale separation. We address this grand challenge by directly bridging large-scale molecular dynamics (MD) simulations with continuum finite-element (FE) models using MISTnetX, a convolutional deep neural network. Trained on MD simulations of shock propagation through complex microstructures, MISTnetX captures shock-microstructure interactions, hotspot formation, and the transition to deflagration, supplying critical sub-grid information to FE simulations of mechanics, shocks, thermal transport, and chemistry. Applied to a synthetic but realistic nanostructured plastic-bonded RDX composite, MISTnetX enables parameter-free prediction of the full run-to-detonation transition. △ Less

Submitted 26 May, 2026; originally announced May 2026.

A Data-Driven Parametric Reduced-Order Chemical Kinetics Model Derived from Atomistic Simulations

Authors: Michael N. Sakano, Alejandro Strachan

Abstract: Coarse-grained modeling in molecular simulations serves not only to extend accessible time and length scales beyond atomistic limits, but also to reduce high-dimensional chemical data to low-dimensional representations that expose the underlying latent structure. In the context of energetic materials, reduced-order chemical kinetics models are essential for describing thermally driven decompositio… ▽ More Coarse-grained modeling in molecular simulations serves not only to extend accessible time and length scales beyond atomistic limits, but also to reduce high-dimensional chemical data to low-dimensional representations that expose the underlying latent structure. In the context of energetic materials, reduced-order chemical kinetics models are essential for describing thermally driven decomposition, deflagration, and detonation. Recent data-driven approaches based on machine learning and dimensionality reduction have shown promise for constructing such models directly from atomistic simulations; however, when reaction pathways vary strongly with thermodynamic conditions, these methods can produce latent representations that are difficult to interpret physically or extrapolate reliably. Here, we introduce a parametric, temperature-dependent autoencoder framework that learns a unified reduced-order description of chemical decomposition across a wide range of temperatures within a single model. Physical interpretability is enforced through non-negativity constraints and a softmax activation, enabling the latent variables to be directly associated with additive chemical components and their relative contributions. Reaction kinetics and heat-release parameters are optimized simultaneously within the neural-network architecture, providing a self-consistent coupling between chemical evolution and energetics. The proposed approach yields significantly improved reconstruction accuracy compared to a state-of-the-art dimensionality-reduction method, as quantified by reductions in mean-squared error, while preserving a physically meaningful latent representation. These results demonstrate that parametric, interpretable machine-learning models can provide robust reduced-order chemical kinetics suitable for multiscale modeling of complex reactive systems. △ Less

Submitted 5 May, 2026; originally announced May 2026.

Comments: Main Text: 21 pages, 8 figures; Supplementary Material: 11 pages, 10 figures

Open WDVV equations and $\bigvee$-systems

Authors: Alessandro Proserpio, Ian A. B. Strachan

Abstract: The idea of a $\bigvee$-system was introduced by Veselov in the study of rational solutions of the WDVV equations of associativity. These are algebraic/geometric conditions on the set of covectors that appear in rational solutions to the WDVV equations. Here, this idea is generalized to open WDVV equations, which are an additional set of PDEs originating from open Gromow-Witten Theory. We develop… ▽ More The idea of a $\bigvee$-system was introduced by Veselov in the study of rational solutions of the WDVV equations of associativity. These are algebraic/geometric conditions on the set of covectors that appear in rational solutions to the WDVV equations. Here, this idea is generalized to open WDVV equations, which are an additional set of PDEs originating from open Gromow-Witten Theory. We develop -- for rank-one extensions -- algebraic/geometric conditions on the covectors that supplement the $\bigvee$-system to give rational solutions to the open WDVV equations. Examples, and the relation to superpotentials and to Dubrovin almost-duality, are given. △ Less

Submitted 15 April, 2026; originally announced April 2026.

Comments: 22 pages; comments welcome!

MSC Class: 53D45 (Primary); 20F55; 35C05; 58J60 (Secondary)

Evaluating LLM-generated code for domain-specific languages: molecular dynamics with LAMMPS

Authors: Ethan Holbrook, Juan C. Verduzco, Alejandro Strachan

Abstract: Large language models (LLMs) are changing the way researchers interact with code and data in scientific computing. While their ability to generate general-purpose code is well established, their effectiveness in producing scientifically valid scripts for domain-specific language (DSLs) remains largely unexplored. We propose an evaluation procedure that enables domain experts to assess the validity… ▽ More Large language models (LLMs) are changing the way researchers interact with code and data in scientific computing. While their ability to generate general-purpose code is well established, their effectiveness in producing scientifically valid scripts for domain-specific language (DSLs) remains largely unexplored. We propose an evaluation procedure that enables domain experts to assess the validity of LLM-generated input files for LAMMPS, a widely used molecular dynamics (MD) code, without requiring deep familiarity with its syntax. The evaluation procedure combines a normalization step that produces canonical input files with an extensible parser for syntax analysis, followed by a reduced-cost execution stage and accuracy checks that isolate common errors before running costly simulations. We apply the pipeline to eight state-of-the-art LLMs across three prompts of increasing complexity. The parser pass rate has improved from 74% to 91% over the past year, but scientific accuracy on coupled multi-step workflows remains limited. Across all 80 scripts evaluated on the most complex prompt, only one was fully correct as generated. We further package the automated stages as a reusable agentic skill that LLMs can invoke during script generation; in a small-scale demonstration, this skill helped two models produce five fully correct scripts out of six across the same three prompts, including the hardest one. The pipeline highlights both the limitations of current LLMs in generating scientific DSLs and a practical path toward integrating them into domain-specific computational ecosystems. △ Less

Submitted 21 May, 2026; v1 submitted 20 March, 2026; originally announced March 2026.

Comments: 19 pages, 5 figures, Supporting Info, 27 total pages

Nuclear Quantum Effects in Multi-Step Condensed Matter Chemistry: A Path Integral Molecular Dynamics Study of Thermal Decomposition

Authors: Jalen Macatangay, Alejandro Strachan

Abstract: Nuclear quantum effects (NQEs) are often central to a predictive understanding of chemical reactions and rates. While their incorporation in gas-phase reactions is well established, studies involving condensed matter often neglect or approximate such effects. To clarify the role of NQEs in multi-step, multi-molecular reactions in a molecular crystal, we compare atomistic simulations of the thermal… ▽ More Nuclear quantum effects (NQEs) are often central to a predictive understanding of chemical reactions and rates. While their incorporation in gas-phase reactions is well established, studies involving condensed matter often neglect or approximate such effects. To clarify the role of NQEs in multi-step, multi-molecular reactions in a molecular crystal, we compare atomistic simulations of the thermal decomposition of the energetic material TATB using path integral molecular dynamics (PIMD), the more approximate quantum thermal bath (QTB), and classical MD (ClMD). PIMD samples the quantum canonical distribution by representing each atom as a string of beads (replicas), while QTB uses a frequency-dependent thermostat to reproduce the Bose-Einstein distribution. We find that PIMD results in faster chemical decomposition of the TATB crystal compared to ClMD, as the initial steps involve hydrogen transfer processes. Interestingly, some of the subsequent reactions (e.g. the formation of N2) occur on identical timescales. The PIMD simulations also predict a reduction in overall activation energy by ~8% as compared to the classical result. As observed in model systems and simple unimolecular gas-phase reactions, the QTB significantly overestimates quantum acceleration of chemical reactions and the reduction in activation energy. A comparison of the kinetic energy operator in PIMD and the centroid dynamics provides insight into the physics behind the differences between the QTB and PIMD results. △ Less

Submitted 11 March, 2026; originally announced March 2026.

Physics-constrained Gaussian Processes for Predicting Shockwave Hugoniot Curves

Authors: George D. Pasparakis, Himanshu Sharma, Rushik Desai, Chunyu Li, Alejandro Strachan, Lori Graham-Brady, Michael D. Shields

Abstract: A physics-constrained Gaussian Process regression framework is developed for predicting shocked material states and their associated uncertainties along the Hugoniot curve using data from a small number of shockwave simulations. The proposed Gaussian process is constrained by the Rankine-Hugoniot jump conditions between the various shocked material states to construct a thermodynamically consisten… ▽ More A physics-constrained Gaussian Process regression framework is developed for predicting shocked material states and their associated uncertainties along the Hugoniot curve using data from a small number of shockwave simulations. The proposed Gaussian process is constrained by the Rankine-Hugoniot jump conditions between the various shocked material states to construct a thermodynamically consistent covariance function. This leads to the formulation of an optimization problem over a small number of interpretable hyperparameters and enables the identification of regime transitions, from a leading elastic wave to trailing plastic and phase transformation waves. Shock Hugoniots are an important measure for understanding material behavior under extreme conditions, including for the development of equations of state and determining material properties such as the Hugoniot Elastic Limit, but they are costly to generate through large-scale molecular dynamics simulations or shock experiments. Under these constraints, the proposed methodology establishes Hugoniot curves from a limited number of molecular dynamics simulations. We consider silicon carbide as a representative material and Molecular Dynamics simulations are performed using a reverse ballistic approach. The framework reproduces the Hugoniot curve with satisfactory accuracy while also quantifying the uncertainty in the predictions using the Gaussian Process posterior. These uncertain Hugoniot predictions can then be used to calibrate equation of state models, estimate material properties, or inform future experimental and/or simulation campaigns. △ Less

Submitted 4 June, 2026; v1 submitted 10 January, 2026; originally announced January 2026.

Multi-Fidelity Predictive Model for Shock Response of Energetic Materials Using Conditional U-Net

Authors: Brian H. Lee, Chunyu Li, Aidan Pantoya, James P. Larentzos, John K. Brennan, Alejandro Strachan

Abstract: Mapping microstructure to properties is central to materials science. Perhaps most famously, the Hall-Petch relationship relates average grain size to strength. More challenging has been deriving relationships for properties that depend on subtle microstructural features and not average properties. One such example is the initiation of energetic materials under dynamical loading, dominated by ener… ▽ More Mapping microstructure to properties is central to materials science. Perhaps most famously, the Hall-Petch relationship relates average grain size to strength. More challenging has been deriving relationships for properties that depend on subtle microstructural features and not average properties. One such example is the initiation of energetic materials under dynamical loading, dominated by energy localization on microstructural features such as pores, cracks, and interfaces. We propose a conditional convolutional neural network to predict the shock-induced temperature field as a function of shock strength, for a wide range of microstructures, and obtained via two different simulation methods. The proposed model, denoted MISTnet2, significantly extends prior work that was limited to a single shock strength, model, and type of microstructure. MISTnet2 can contribute to bridging atomistics with coarse-grain simulations and enable first principles predictions of detonation initiation and safety of this class of materials. △ Less

Submitted 5 January, 2026; originally announced January 2026.

Active Learning Discovery of High Temperature Oxidation Resistant Refractory Complex Concentrated Alloys

Authors: Akhil Bejjipurapu, Sharmila Karumuri, Joseph C. Flanagan, Victoria Tucker, Ilias Bilionis, Alejandro Strachan, Kenneth H. Sandhage, Michael S. Titus

Abstract: Refractory complex concentrated alloys (RCCAs) are of significant interest for advanced high-temperature applications, owing to their broad compositional range and potential for attractive mechanical properties and oxidation resistance. However, their compositional complexity poses significant challenges to conventional alloy discovery methodologies. In this study, an active learning framework is… ▽ More Refractory complex concentrated alloys (RCCAs) are of significant interest for advanced high-temperature applications, owing to their broad compositional range and potential for attractive mechanical properties and oxidation resistance. However, their compositional complexity poses significant challenges to conventional alloy discovery methodologies. In this study, an active learning framework is introduced that integrates Gaussian process regression with Bayesian global optimization to accelerate identification of oxidation-resistant RCCAs. Focusing on aluminum-containing quaternary systems, alloy and oxide descriptors were used to predict oxidation performance at 1000$^\circ$C. Beginning with a dataset of 81 experimentally validated RCCAs, this framework was used to iteratively select alloy batches (five alloys per batch) with optimization based on a balance between exploration and exploitation to minimize associated experimental costs. After six iterations, two alloys were identified (nominal Al$_{30}$Mo$_5$Ti$_{15}$Cr$_{50}$ and Al$_{40}$Mo$_5$Ti$_{30}$Cr$_{25}$) that exhibited specific mass gains less than 1 mg/cm$^2$ at 1000$^\circ$C in air. Both of these alloys formed adherent external $α$-Al$_2$O$_3$ scales and exhibited parabolic oxidation kinetics consistent with diffusion-limited scale growth. Furthermore, our multiobjective analysis demonstrates that these alloys simultaneously achieve high specific hardness ($>0.12$ HV$_{0.5}$m$^3$/kg) and thermal expansion compatibility with thermal barrier coating systems, positioning them as promising bond coat candidates. This work underscores the efficacy of active learning in traversing complex compositional landscapes, and offers a scalable strategy for the development of advanced materials suitable for extreme environments. △ Less

Submitted 17 December, 2025; originally announced December 2025.

Dubrovin duality for open Hurwitz flat F-manifolds

Authors: Alessandro Proserpio, Ian A. B. Strachan

Abstract: We prove that the Dubrovin dual of a Hurwitz Frobenius manifold extends naturally to an F-manifold with compatible flat connection on the universal curve, in the sense of the open WDVV equations. A similar result is proven for the Frobenius manifold itself in arXiv:2503.09258 . This equips the universal curve with two F-manifolds with compatible flat structure, and we study their duality. We show… ▽ More We prove that the Dubrovin dual of a Hurwitz Frobenius manifold extends naturally to an F-manifold with compatible flat connection on the universal curve, in the sense of the open WDVV equations. A similar result is proven for the Frobenius manifold itself in arXiv:2503.09258 . This equips the universal curve with two F-manifolds with compatible flat structure, and we study their duality. We show that they combine into a bi-flat F-manifold. Conditions on open WDVV solutions imposed in previous work are retrieved in this setting, thus providing them with a geometrical meaning. Finally, explicit examples are computed. For Saito Frobenius manifolds of types $A$ and $D$, the extended prepotentials coincide with open WDVV solutions computed independently, whereas even the existence of the solution in type $E$ had not been previously discussed. On the other hand, new non-homogeneous solutions are constructed by duality. △ Less

Submitted 9 December, 2025; originally announced December 2025.

MSC Class: 53D45 (Primary) 37K10; 14H70 (Secondary)

Machine learning descriptors for predicting the high temperature oxidation of refractory complex concentrated alloys

Authors: Akhil Bejjipurapu, Alejandro Strachan, Kenneth H. Sandhage, Michael S. Titus

Abstract: Refractory Complex Concentrated Alloys (RCCAs) can exhibit exceptional high-temperature strength, making such alloys promising candidates for high-temperature structural applications. However, current RCCAs do not possess the high-temperature oxidation resistance required to survive in oxidizing environments for more than a few hours at or above 1000$^\circ$C, without relying primarily on an envir… ▽ More Refractory Complex Concentrated Alloys (RCCAs) can exhibit exceptional high-temperature strength, making such alloys promising candidates for high-temperature structural applications. However, current RCCAs do not possess the high-temperature oxidation resistance required to survive in oxidizing environments for more than a few hours at or above 1000$^\circ$C, without relying primarily on an environmental barrier coating. Here, we present a machine-learning framework designed to predict the oxidation-induced specific mass changes of RCCAs exposed for 24 h at 1000$^\circ$C in air, in order to support the search for oxidation-resistant alloys over a wide range of compositions. A database was constructed of experimental specific mass change data, upon oxidation at 900-1000$^\circ$C for 24 h in air, for 77 compositions comprised of simple elements, binary alloys, and higher-order elemental systems. We then developed a Gaussian Process Regression (GPR) model with physics-informed descriptors based on oxidation products, capturing the fundamental chemistry of oxide formation and stability. Application of this GPR model to the database yielded a MAE (mean absolute error) test score of 5.78 mg/cm$^2$, which was a significant improvement in accuracy relative to models only utilizing traditional alloy-based descriptors. Our model was used to screen over 5,100 quaternary RCCAs, revealing compositions with significantly lower predicted specific mass changes compared to existing literature sources. Overall, this work establishes a versatile and efficient strategy to accelerate the discovery of next-generation RCCAs with enhanced resistance to extreme environments. △ Less

Submitted 2 November, 2025; originally announced November 2025.

Bridging the Synthesizability Gap in Perovskites by Combining Computations, Literature Data, and PU Learning

Authors: Rushik Desai, Junyeong Ahn, Alejandro Strachan, Arun Mannodi-Kanakkithodi

Abstract: Among emerging energy materials, halide and chalcogenide perovskites have garnered significant attention over the last decade owing to the abundance of their constituent species, low manufacturing costs, and their highly tunable composition-structure-property space. Navigating the vast perovskite compositional landscape is possible using density functional theory (DFT) computations, but they are n… ▽ More Among emerging energy materials, halide and chalcogenide perovskites have garnered significant attention over the last decade owing to the abundance of their constituent species, low manufacturing costs, and their highly tunable composition-structure-property space. Navigating the vast perovskite compositional landscape is possible using density functional theory (DFT) computations, but they are not easily extended to predictions of the synthesizability of new materials and their properties. As a result, only a limited number of compositions identified to have desirable optoelectronic properties from these calculations have been realized experimentally. One way to bridge this gap is by learning from the experimental literature about how the perovskite composition-structure space relates to their likelihood of laboratory synthesis. Here, we present our efforts in combining high-throughput DFT data with experimental labels collected from the literature to train classifier models employing various materials descriptors to forecast the synthesizability of any given perovskite compound. Our framework utilizes the positive and unlabeled (PU) learning strategy and makes probabilistic estimates of the synthesis likelihood based on DFT- computed energies and the prior existence of similar synthesized compounds. Our data and models can be readily accessed via a Findable, Accessible, Interoperable, and Reproducible (FAIR) nanoHUB tool. △ Less

Submitted 10 November, 2025; v1 submitted 7 October, 2025; originally announced October 2025.

Exploration of Hexagonal, Layered Carbides and Nitrides as Ultra-High Temperature Ceramics

Authors: Kat Nykiel, Brian Wyatt, Babak Anasori, Alejandro Strachan

Abstract: Layered, hexagonal crystal structures, like zeta and eta phases, play an important role in ultra-high temperature ceramics, often significantly increasing toughness of carbide composites. Despite their importance open questions remain about their structure, stability, and compositional pervasiveness. We use high-throughput density functional theory to characterize the thermodynamic stability and e… ▽ More Layered, hexagonal crystal structures, like zeta and eta phases, play an important role in ultra-high temperature ceramics, often significantly increasing toughness of carbide composites. Despite their importance open questions remain about their structure, stability, and compositional pervasiveness. We use high-throughput density functional theory to characterize the thermodynamic stability and elastic constants of layered carbides and nitrides M$_{n+1}$X$_{n}$ with $n$ = 1, 2, and 3, $M$ = Ta, Ti, Hf, Zr, Nb, Mo, V, W, Sc, Cr, Mn and $X$ = C, N. The stacking sequences explored are inspired by the possible use of MXenes as precursors to enable relatively low temperature processing of high-temperature ceramics. We identified 67 new hexagonal, layered materials with thermal stability comparable or better than previously observed zeta phases. To assess their potential for high temperature applications, we used machine learning and physics-based models with DFT inputs to predict their melting temperatures and discovered several candidates on par with the current state of the art zeta-like phases and five with predicted melting temperatures above 2500 K. The findings expand the range of chemistries and structures for high-temperature applications. △ Less

Submitted 25 August, 2025; originally announced August 2025.

Predictive models for strain energy in condensed phase reactions

Authors: Baptiste Martin, Shukai Yao, Chunyu Li, Anthony Bocahut, Matthew Jackson, Alejandro Strachan

Abstract: Molecular modeling of thermally activated chemistry in condensed phases is essential to understand polymerization, depolymerization, and other processing steps of molecular materials. Current methods typically combine molecular dynamics (MD) simulations to describe short-time relaxation with a stochastic description of predetermined chemical reactions. Possible reactions are often selected on the… ▽ More Molecular modeling of thermally activated chemistry in condensed phases is essential to understand polymerization, depolymerization, and other processing steps of molecular materials. Current methods typically combine molecular dynamics (MD) simulations to describe short-time relaxation with a stochastic description of predetermined chemical reactions. Possible reactions are often selected on the basis of geometric criteria, such as a capture distance between reactive atoms. Although these simulations have provided valuable insight, the approximations used to determine possible reactions often lead to significant molecular strain and unrealistic structures. We show that the local molecular environment surrounding the reactive site plays a crucial role in determining the resulting molecular strain energy and, in turn, the associated reaction rates. We develop a graph neural network capable of predicting the strain energy associated with a cyclization reaction from the pre-reaction, local, molecular environment surrounding the reactive site. The model is trained on a large dataset of condensed-phase reactions during the activation of polyacrylonitrile (PAN) obtained from MD simulations and can be used to adjust relative reaction rates in condensed systems and advance our understanding of thermally activated chemical processes in complex materials △ Less

Submitted 7 January, 2026; v1 submitted 21 August, 2025; originally announced August 2025.

A collaborative digital twin built on FAIR data and compute infrastructure

Authors: Thomas M. Deucher, Juan C. Verduzco, Michael Titus, Alejandro Strachan

Abstract: The integration of machine learning with automated experimentation in self-driving laboratories (SDL) offers a powerful approach to accelerate discovery and optimization tasks in science and engineering applications. When supported by findable, accessible, interoperable, and reusable (FAIR) data infrastructure, SDLs with overlapping interests can collaborate more effectively. This work presents a… ▽ More The integration of machine learning with automated experimentation in self-driving laboratories (SDL) offers a powerful approach to accelerate discovery and optimization tasks in science and engineering applications. When supported by findable, accessible, interoperable, and reusable (FAIR) data infrastructure, SDLs with overlapping interests can collaborate more effectively. This work presents a distributed SDL implementation built on nanoHUB services for online simulation and FAIR data management. In this framework, geographically dispersed collaborators conducting independent optimization tasks contribute raw experimental data to a shared central database. These researchers can then benefit from analysis tools and machine learning models that automatically update as additional data become available. New data points are submitted through a simple web interface and automatically processed using a nanoHUB Sim2L, which extracts derived quantities and indexes all inputs and outputs in a FAIR data repository called ResultsDB. A separate nanoHUB workflow enables sequential optimization using active learning, where researchers define the optimization objective, and machine learning models are trained on-the-fly with all existing data, guiding the selection of future experiments. Inspired by the concept of ``frugal twin", the optimization task seeks to find the optimal recipe to combine food dyes to achieve the desired target color. With easily accessible and inexpensive materials, researchers and students can set up their own experiments, share data with collaborators, and explore the combination of FAIR data, predictive ML models, and sequential optimization. The tools introduced are generally applicable and can easily be extended to other optimization problems. △ Less

Submitted 24 June, 2025; originally announced July 2025.

Comments: 10 pages, 5 figures

Spall strength of symmetric tilt grain boundaries in 6H silicon carbide

Authors: Chunyu Li, Alejandro Strachan

Abstract: Characterizing microstructural effects on the dynamical response of materials is challenging due to the extreme conditions and the short timescales involved. For example, little is known about how grain boundary characteristics affect spall strength. This study explores 6H-SiC bicrystals under shock waves via large-scale molecular dynamics simulations. We focused on symmetric tilt grain boundaries… ▽ More Characterizing microstructural effects on the dynamical response of materials is challenging due to the extreme conditions and the short timescales involved. For example, little is known about how grain boundary characteristics affect spall strength. This study explores 6H-SiC bicrystals under shock waves via large-scale molecular dynamics simulations. We focused on symmetric tilt grain boundaries with a wide range of misorientations and found that spall strength and dynamical fracture surface energy are strongly affected by the grain boundary microstructure, especially the excess free volume. Grain boundary energy also plays a considerable role. As expected, low-angle grain boundaries tend to have higher spallation strengths. We also extracted cohesive models for the dynamical strength of bulk systems and grain boundaries that can be used in continuum simulations. △ Less

Submitted 24 April, 2025; originally announced April 2025.

Journal ref: J. Appl. Phys. 21 July 2025; 138 (3): 035901

Harnessing Machine Learning for Quantum-Accurate Predictions of Non-Equilibrium Behavior in 2D Materials

Authors: Yue Zhang, Robert J. Appleton, Kui Lin, Megan J. McCarthy, Jeffrey T. Paci, Subramanian K. R. S. Sankaranarayanan, Alejandro Strachan, Horacio D. Espinosa

Abstract: Accurately predicting the non-equilibrium mechanical properties of two-dimensional (2D) materials is essential for understanding their deformation, thermo-mechanical properties, and failure mechanisms. In this study, we parameterize and evaluate two machine learning (ML) interatomic potentials, SNAP and Allegro, for modeling the non-equilibrium behavior of monolayer MoSe2. Using a density function… ▽ More Accurately predicting the non-equilibrium mechanical properties of two-dimensional (2D) materials is essential for understanding their deformation, thermo-mechanical properties, and failure mechanisms. In this study, we parameterize and evaluate two machine learning (ML) interatomic potentials, SNAP and Allegro, for modeling the non-equilibrium behavior of monolayer MoSe2. Using a density functional theory (DFT) derived dataset, we systematically compare their accuracy and transferability against the physics-based Tersoff force field. Our results show that SNAP and Allegro significantly outperform Tersoff, achieving near-DFT accuracy while maintaining computational efficiency. Allegro surpasses SNAP in both accuracy and efficiency due to its advanced neural network architecture. Both ML potentials demonstrate strong transferability, accurately predicting out-of-sample properties such as surface stability, inversion domain formation, and fracture toughness. Unlike Tersoff, SNAP and Allegro reliably model temperature-dependent edge stabilities and phase transformation pathways, aligning closely with DFT benchmarks. Notably, their fracture toughness predictions closely match experimental measurements, reinforcing their suitability for large-scale simulations of mechanical failure in 2D materials. This study establishes ML-based force fields as a powerful alternative to traditional potentials for modeling non-equilibrium mechanical properties in 2D materials. △ Less

Submitted 13 April, 2025; originally announced April 2025.

Comments: 23 pages, 5 figures, Supplementary Information (5 figures and two tables)

Data Fusion of Deep Learned Molecular Embeddings for Property Prediction

Authors: Robert J Appleton, Brian C Barnes, Alejandro Strachan

Abstract: Data-driven approaches such as deep learning can result in predictive models for material properties with exceptional accuracy and efficiency. However, in many applications, data is sparse, severely limiting their accuracy and applicability. To improve predictions, techniques such as transfer learning and multitask learning have been used. The performance of multitask learning models depends on th… ▽ More Data-driven approaches such as deep learning can result in predictive models for material properties with exceptional accuracy and efficiency. However, in many applications, data is sparse, severely limiting their accuracy and applicability. To improve predictions, techniques such as transfer learning and multitask learning have been used. The performance of multitask learning models depends on the strength of the underlying correlations between tasks and the completeness of the data set. Standard multitask models tend to underperform when trained on sparse data sets with weakly correlated properties. To address this gap, we fuse deep-learned embeddings generated by independent pretrained single-task models, resulting in a multitask model that inherits rich, property-specific representations. By reusing (rather than retraining) these embeddings, the resulting fused model outperforms standard multitask models and can be extended with fewer trainable parameters. We demonstrate this technique on a widely used benchmark data set of quantum chemistry data for small molecules as well as a newly compiled sparse data set of experimental data collected from literature and our own quantum chemistry and thermochemical calculations. △ Less

Submitted 28 October, 2025; v1 submitted 9 April, 2025; originally announced April 2025.

Comments: J. Chem. Inf. Model. 2025

Modeling Framework to Predict Melting Dynamics at Microstructural Defects in TNT-HMX High Explosive Composites

Authors: Ethan Holbrook, Matthew P. Kroonblawd, Brenden W. Hamilton, H. Keo Springer, Alejandro Strachan

Abstract: Many high explosive (HE) formulations are composite materials whose microstructure is understood to impact functional characteristics. Interfaces are known to mediate the formation of hot spots that control their safety and initiation. To study such processes at molecular scales, we developed all-atom force fields (FFs) for Octol, a prototypical HE formulation comprised of TNT (2,4,6-trinitrotolue… ▽ More Many high explosive (HE) formulations are composite materials whose microstructure is understood to impact functional characteristics. Interfaces are known to mediate the formation of hot spots that control their safety and initiation. To study such processes at molecular scales, we developed all-atom force fields (FFs) for Octol, a prototypical HE formulation comprised of TNT (2,4,6-trinitrotoluene) and HMX (octahydro-1,3,5,7-tetranitro-1,3,5,7-tetrazocine). We extended a FF for TNT and recasted it in a form that can be readily combined with a well-established FF for HMX. The resulting FF was extensively validated against experimental results and density functional theory calculations. We applied the new combined TNT-HMX FF to predict and rank surface and interface energies, which indicate that there is an energetic driver for coarsening of microstructural grains in TNT-HMX composites. Finally, we assess the impact of several microstructural environments on the dynamic melting of TNT crystal under ultrafast thermal loading. We find that both free surfaces and planar material interfaces are effective nucleation points for TNT melting. However, MD simulations show that TNT crystal is prone to superheating by at least 50 K on sub-nanosecond timescales and that the degree of superheating is inversely correlated with surface and interface energy. The modeling framework presented here will enable future studies on hot spot formation processes in accident scenarios that are governed by strong coupling between microstructural interfaces, material mechanics, momentum and energy transport, phase transitions, and chemistry. △ Less

Submitted 30 March, 2025; originally announced March 2025.

Comments: 56 pages, 12 figures

Exploring the defect landscape and dopability of chalcogenide perovskite BaZrS3

Authors: Rushik Desai, Shubhanshu Agarwal, Kiruba Catherine Vincent, Alejandro Strachan, Rakesh Agrawal, Arun Mannodi-Kanakkithodi

Abstract: BaZrS3 is a chalcogenide perovskite that has shown promise as a photovoltaic absorber, but its performance is limited because of defects and impurities that have a direct influence on carrier concentrations. Functional dopants that show lower donor-type or acceptor-type formation energies than naturally occurring defects can help tune the optoelectronic properties of BaZrS3. In this work, we appli… ▽ More BaZrS3 is a chalcogenide perovskite that has shown promise as a photovoltaic absorber, but its performance is limited because of defects and impurities that have a direct influence on carrier concentrations. Functional dopants that show lower donor-type or acceptor-type formation energies than naturally occurring defects can help tune the optoelectronic properties of BaZrS3. In this work, we applied first principles computations to comprehensively investigate the defect landscape of BaZrS3, including all intrinsic defects and a set of selected impurities and dopants. BaZrS3 intrinsically exhibits n-type equilibrium conductivity under both S-poor and S-rich conditions, which remains largely unchanged in the presence of O and H impurities. La and Nb dopants created stable donor-type defects, which made BaZrS3 even more n-type, whereas As and P dopants formed amphoteric defects with relatively high formation energies. This work highlights the difficulty of creating p-type BaZrS3 owing to the low formation energies of donor defects, both intrinsic and extrinsic. Defect formation energies were also used to compute expected defect concentrations and make comparisons with experimentally reported values. Our dataset of defects in BaZrS3 paves the path for training machine learning models to subsequently perform larger-scale prediction and screening of defects and dopants across many chalcogenide perovskites, including cation-site or anion-site alloys. △ Less

Submitted 7 April, 2025; v1 submitted 27 January, 2025; originally announced January 2025.

Dubrovin duality and mirror symmetry for ADE resolutions

Authors: Andrea Brini, Jingxiang Ma, Ian A. B. Strachan

Abstract: We show that, under Dubrovin's notion of ''almost'' duality, the Frobenius manifold structure on the orbit spaces of the extended affine Weyl groups of type $\mathrm{ADE}$ is dual, for suitable choices of weight markings, to the equivariant quantum cohomology of the minimal resolution of the du Val singularity of the same Dynkin type. We also provide a uniform Lie-theoretic construction of Landau-… ▽ More We show that, under Dubrovin's notion of ''almost'' duality, the Frobenius manifold structure on the orbit spaces of the extended affine Weyl groups of type $\mathrm{ADE}$ is dual, for suitable choices of weight markings, to the equivariant quantum cohomology of the minimal resolution of the du Val singularity of the same Dynkin type. We also provide a uniform Lie-theoretic construction of Landau-Ginzburg mirrors for the quantum cohomology of $\mathrm{ADE}$ resolutions. The mirror B-model is described by a one-dimensional LG superpotential associated to the spectral curve of the $\widehat{\mathrm{ADE}}$ affine relativistic Toda chain. △ Less

Submitted 10 January, 2025; originally announced January 2025.

Comments: 26 pages. Ancillary files of this arXiv submission are accessible in the directory anc/ included in the source of this manuscript. The directory contains the Wolfram Language code used to verify Proposition 3.16 for the exceptional series. A Mathematica package verifying the main conjecture in IMRN 19 (2003), 1035-1051 is also included

Journal ref: Proc. R. Soc. A.481 (2025) 0047

Integrating HW/SW Functionality for Flexible Wireless Radio

Authors: Alexander Strachan, Nigel Topham

Abstract: Current methods of implementing wireless radio typically take one of two forms; either dedicated fixed-function hardware, or pure Software Defined Radio (SDR). Fixed function hardware is efficient, but being specific to each radio standard it lacks flexibility, whereas Software Defined Radio is highly flexible but requires powerful processors to meet real-time performance constraints. This paper p… ▽ More Current methods of implementing wireless radio typically take one of two forms; either dedicated fixed-function hardware, or pure Software Defined Radio (SDR). Fixed function hardware is efficient, but being specific to each radio standard it lacks flexibility, whereas Software Defined Radio is highly flexible but requires powerful processors to meet real-time performance constraints. This paper presents a hybrid hardware/software approach that aims to combine the flexibility of SDR with the efficiency of dedicated hardware solutions. We evaluate this approach by simulating five variants of the IEEE 802.15.4 protocol, commonly known as Zigbee, and demonstrate the range of performance and power consumption characteristics for different accelerator and software configurations. Across the spectrum of configurations we see power consumption varies from 8% to 38% of a dedicated hardware implementation, and show how the hybrid approach allows a new modulation standard to be retrofitted to an existing design, with only a modest increase in power consumption. △ Less

Submitted 13 December, 2024; originally announced December 2024.

Comments: Computer Science & Information Technology (CS & IT) ISSN : 2231 - 5403 Volume 14, Number 23, November 2024

Diagonal invariants and genus-zero Hurwitz Frobenius manifolds

Authors: Alessandro Proserpio, Ian A. B. Strachan

Abstract: The Frobenius manifold structure on the space of rational functions with multiple simple poles is constructed. In particular, the dependence of the Saito-flat coordinates on the flat coordinates of the intersection form is studied. While some of the individual flat coordinates are complicated rational functions, they appear in the prepotential in certain combinations known as diagonal invariants,… ▽ More The Frobenius manifold structure on the space of rational functions with multiple simple poles is constructed. In particular, the dependence of the Saito-flat coordinates on the flat coordinates of the intersection form is studied. While some of the individual flat coordinates are complicated rational functions, they appear in the prepotential in certain combinations known as diagonal invariants, which turn out to be polynomial. Two classes are studied in more detail. These are generalisations of the Coxeter and extended-affine-Weyl orbits space for the group $W=W(A_\ell)\,.$ An invariant theory is also developed. △ Less

Submitted 9 September, 2025; v1 submitted 6 December, 2024; originally announced December 2024.

Comments: Final version, accepted for publication on Selecta Math

MSC Class: 53D45 (Primary); 20F55; 37K25 (Secondary)

Journal ref: Selecta Math. New Ser. 32, 13 (2026)

Thermodynamic Fidelity of Generative Models for Ising System

Authors: Brian H. Lee, Kat Nykiel, Ava E. Hallberg, Brice Rider, Alejandro Strachan

Abstract: Machine learning has become a central technique for modeling in science and engineering, either complementing or as surrogates to physics-based models. Significant efforts have recently been devoted to models capable of predicting field quantities but the limitations of current state-of-the-art models in describing complex physics are not well understood. We characterize the ability of generative… ▽ More Machine learning has become a central technique for modeling in science and engineering, either complementing or as surrogates to physics-based models. Significant efforts have recently been devoted to models capable of predicting field quantities but the limitations of current state-of-the-art models in describing complex physics are not well understood. We characterize the ability of generative diffusion models and generative adversarial networks (GAN) to describe the Ising model. We find diffusion models trained using equilibrium configurations obtained using Metropolis Monte Carlo for a range of temperatures around the critical temperature can capture average thermodynamic variables across the phase transformation and extrapolate to higher and lower temperatures. The model also captures the overall trends of physical properties associated with fluctuations (specific heat and susceptibility) except at the non-ergodic low temperatures and non-trivial scale-free correlations at the critical temperature, albeit with some difference in the critical exponent compared to Monte Carlo simulations. GANs perform more poorly on thermodynamic properties and are susceptible to mode-collapse without careful training. This investigation highlights the potential and limitations of generative models in capturing the complex phenomena associated with certain physical systems. △ Less

Submitted 4 December, 2024; originally announced December 2024.

Accelerating active learning materials discovery with FAIR data and workflows: a case study for alloy melting temperatures

Authors: Mohnish Harwani, Juan C. Verduzco, Brian H. Lee, Alejandro Strachan

Abstract: Active learning (AL) is a powerful sequential optimization approach that has shown great promise in the discovery of new materials. However, a major challenge remains the acquisition of the initial data and the development of workflows to generate new data at each iteration. In this study, we demonstrate a significant speedup in an optimization task by reusing a published simulation workflow avail… ▽ More Active learning (AL) is a powerful sequential optimization approach that has shown great promise in the discovery of new materials. However, a major challenge remains the acquisition of the initial data and the development of workflows to generate new data at each iteration. In this study, we demonstrate a significant speedup in an optimization task by reusing a published simulation workflow available for online simulations and its associated data repository, where the results of each workflow run are automatically stored. Both the workflow and its data follow FAIR (findable, accessible, interoperable, and reusable) principles using nanoHUB's infrastructure. The workflow employs molecular dynamics to calculate the melting temperature of multi-principal component alloys. We leveraged all prior data not only to develop an accurate machine learning model to start the sequential optimization but also to optimize the simulation parameters and accelerate convergence. Prior work showed that finding the alloy composition with the highest melting temperature required testing 15 alloy compositions, and establishing the melting temperature for each composition took, on average, 4 simulations. By developing a workflow that utilizes the FAIR data in the nanoHUB database, we reduced the number of simulations per composition to one and found the alloy with the lowest melting temperature testing only three compositions. This second optimization, therefore, shows a speedup of 10x as compared to models that do not access the FAIR databases. △ Less

Submitted 20 November, 2024; originally announced November 2024.

GPT-4o reads the mind in the eyes

Authors: James W. A. Strachan, Oriana Pansardi, Eugenio Scaliti, Marco Celotto, Krati Saxena, Chunzhi Yi, Fabio Manzi, Alessandro Rufo, Guido Manzi, Michael S. A. Graziano, Stefano Panzeri, Cristina Becchio

Abstract: Large Language Models (LLMs) are capable of reproducing human-like inferences, including inferences about emotions and mental states, from text. Whether this capability extends beyond text to other modalities remains unclear. Humans possess a sophisticated ability to read the mind in the eyes of other people. Here we tested whether this ability is also present in GPT-4o, a multimodal LLM. Using tw… ▽ More Large Language Models (LLMs) are capable of reproducing human-like inferences, including inferences about emotions and mental states, from text. Whether this capability extends beyond text to other modalities remains unclear. Humans possess a sophisticated ability to read the mind in the eyes of other people. Here we tested whether this ability is also present in GPT-4o, a multimodal LLM. Using two versions of a widely used theory of mind test, the Reading the Mind in Eyes Test and the Multiracial Reading the Mind in the Eyes Test, we found that GPT-4o outperformed humans in interpreting mental states from upright faces but underperformed humans when faces were inverted. While humans in our sample showed no difference between White and Non-white faces, GPT-4o's accuracy was higher for White than for Non-white faces. GPT-4o's errors were not random but revealed a highly consistent, yet incorrect, processing of mental-state information across trials, with an orientation-dependent error structure that qualitatively differed from that of humans for inverted faces but not for upright faces. These findings highlight how advanced mental state inference abilities and human-like face processing signatures, such as inversion effects, coexist in GPT-4o alongside substantial differences in information processing compared to humans. △ Less

Submitted 30 October, 2024; v1 submitted 29 October, 2024; originally announced October 2024.

Multi-Task Multi-Fidelity Learning of Properties for Energetic Materials

Authors: Robert J. Appleton, Daniel Klinger, Brian H. Lee, Michael Taylor, Sohee Kim, Samuel Blankenship, Brian C. Barnes, Steven F. Son, Alejandro Strachan

Abstract: Data science and artificial intelligence are playing an increasingly important role in the physical sciences. Unfortunately, in the field of energetic materials data scarcity limits the accuracy and even applicability of ML tools. To address data limitations, we compiled multi-modal data: both experimental and computational results for several properties. We find that multi-task neural networks ca… ▽ More Data science and artificial intelligence are playing an increasingly important role in the physical sciences. Unfortunately, in the field of energetic materials data scarcity limits the accuracy and even applicability of ML tools. To address data limitations, we compiled multi-modal data: both experimental and computational results for several properties. We find that multi-task neural networks can learn from multi-modal data and outperform single-task models trained for specific properties. As expected, the improvement is more significant for data-scarce properties. These models are trained using descriptors built from simple molecular information and can be readily applied for large-scale materials screening to explore multiple properties simultaneously. This approach is widely applicable to fields outside energetic materials. △ Less

Submitted 21 August, 2024; originally announced August 2024.

Comments: 16 pages, 4 figures, 2 tables

Legendre transforms for type $A_{n}$ and $B_{n}$ $\vee$-systems

Authors: Misha Feigin, Leo Kaminski, Ian A. B. Strachan

Abstract: The Witten-Dijkgraaf-Verlinde-Verlinde (WDVV) equations have a rich structure related to the theory of Frobenius manifolds, with many known families of solutions. A Legendre transformation is a symmetry of the WDVV equations, introduced by Dubrovin. We explicitly compute the results of a Legendre transformation applied to $A_n$- and $B_n$-type multi-parameter rational solutions, relating them to k… ▽ More The Witten-Dijkgraaf-Verlinde-Verlinde (WDVV) equations have a rich structure related to the theory of Frobenius manifolds, with many known families of solutions. A Legendre transformation is a symmetry of the WDVV equations, introduced by Dubrovin. We explicitly compute the results of a Legendre transformation applied to $A_n$- and $B_n$-type multi-parameter rational solutions, relating them to known and new trigonometric solutions. △ Less

Submitted 29 July, 2024; originally announced July 2024.

Comments: 28 pages

Regular F -manifolds with eventual identities

Authors: Sara Perletti, Ian A. B. Strachan

Abstract: Given an F-manifold one may construct a dual multiplication (generalizing the idea of an almost-dual Frobenius manifold introduced by Dubrovin) using a so-called eventual identity, the definition of which ensure that the dual object is also an F-manifold. In this paper we solve the equations for an eventual identity for a regular (so non-semi-simple) F-manifold and construct a dual coordinate syst… ▽ More Given an F-manifold one may construct a dual multiplication (generalizing the idea of an almost-dual Frobenius manifold introduced by Dubrovin) using a so-called eventual identity, the definition of which ensure that the dual object is also an F-manifold. In this paper we solve the equations for an eventual identity for a regular (so non-semi-simple) F-manifold and construct a dual coordinate system in which dual multiplication is preserved. As an application, families of Nijenhuis operators are constructed. △ Less

Submitted 8 July, 2024; originally announced July 2024.

Comments: 24 pages

MSC Class: 53D45

Influence of Polymer on Shock-Induced Pore Collapse: Hotspot Criticality through Reactive Molecular Dynamics

Authors: Jalen Macatangay, Chunyu Li, Alejandro Strachan

Abstract: The shock initiation of energetic materials is mediated by the localization of mechanical energy into hotspots. These originate through the interaction of the shock and material microstructure; the most potent hotspots are formed by the collapse of porosity. Recent work using molecular dynamics (MD) has shed light on the molecular mechanisms responsible for the shock-to-deflagration transition fol… ▽ More The shock initiation of energetic materials is mediated by the localization of mechanical energy into hotspots. These originate through the interaction of the shock and material microstructure; the most potent hotspots are formed by the collapse of porosity. Recent work using molecular dynamics (MD) has shed light on the molecular mechanisms responsible for the shock-to-deflagration transition following pore collapse in pure energetic materials. However, explosive formulations are composites of energetic crystals and a polymer binder, which differs from the prior focus on pure materials. The role of polymer phases on hotspot formation and its criticality is not well-understood. We use reactive MD simulations to investigate the role of polystyrene and polyvinyl nitrate films around pores in the shock-induced pore collapse of RDX. The polymer affects the hotspots' temperature and their criticality. While the presence of inert polymer often delays or hinders chemical reactions of the energetic material, certain geometries accelerate chemistry. The simulations provide a mechanistic understanding of these phenomena. △ Less

Submitted 24 September, 2024; v1 submitted 3 May, 2024; originally announced May 2024.

Graph neural network coarse-grain force field for the molecular crystal RDX

Authors: Brian H. Lee, James P. Larentzos, John K. Brennan, Alejandro Strachan

Abstract: Condense phase molecular systems organize in wide range of distinct molecular configurations, including amorphous melt and glass as well as crystals often exhibiting polymorphism, that originate from their intricate intra- and intermolecular forces. While accurate coarse-grain (CG) models for these materials are critical to understand phenomena beyond the reach of all-atom simulations, current mod… ▽ More Condense phase molecular systems organize in wide range of distinct molecular configurations, including amorphous melt and glass as well as crystals often exhibiting polymorphism, that originate from their intricate intra- and intermolecular forces. While accurate coarse-grain (CG) models for these materials are critical to understand phenomena beyond the reach of all-atom simulations, current models cannot capture the diversity of molecular structures. We introduce a generally applicable approach to develop CG force fields for molecular crystals combining graph neural networks (GNN) and data from an all-atom simulations and apply it to the high-energy density material RDX. We address the challenge of expanding the training data with relevant configurations via an iterative procedure that performs CG molecular dynamics of processes of interest and reconstructs the atomistic configurations using a pre-trained neural network decoder. The multi-site CG model uses a GNN architecture constructed to satisfy translational invariance and rotational covariance for forces. The resulting model captures both crystalline and amorphous states for a wide range of temperatures and densities. △ Less

Submitted 22 March, 2024; originally announced March 2024.

Lennard Jones Token: a blockchain solution to scientific data curation

Authors: Brian H. Lee, Alejandro Strachan

Abstract: Data science and artificial intelligence have become an indispensable part of scientific research. While such methods rely on high-quality and large quantities of machine-readable scientific data, the current scientific data infrastructure faces significant challenges that limit effective data curation and sharing. These challenges include insufficient return on investment for researchers to share… ▽ More Data science and artificial intelligence have become an indispensable part of scientific research. While such methods rely on high-quality and large quantities of machine-readable scientific data, the current scientific data infrastructure faces significant challenges that limit effective data curation and sharing. These challenges include insufficient return on investment for researchers to share quality data, logistical difficulties in maintaining long-term data repositories, and the absence of standardized methods for evaluating the relative importance of various datasets. To address these issues, this paper presents the Lennard Jones Token, a blockchain-based proof-of-concept solution implemented on the Ethereum network. The token system incentivizes users to submit optimized structures of Lennard Jones particles by offering token rewards, while also charging for access to these valuable structures. Utilizing smart contracts, the system automates the evaluation of submitted data, ensuring that only structures with energies lower than those in the existing database for a given cluster size are rewarded. The paper explores the details of the Lennard Jones Token as a proof of concept and proposes future blockchain-based tokens aimed at enhancing the curation and sharing of scientific data. △ Less

Submitted 1 December, 2023; originally announced December 2023.

Mass uptake during oxidation of metallic alloys: literature data collection, analysis, and FAIR sharing

Authors: Saswat Mishra, Sharmila Karumuri, Vincent Mika, Collin Scott, Chadwick Choy, Kenneth H. Sandhage, Ilias Bilionis, Michael S. Titus, Alejandro Strachan

Abstract: The area-normalized change of mass ($Δ$m/A) with time during the oxidation of metallic alloys is commonly used to assess oxidation resistance. Analyses of such data can also aid in evaluating underlying oxidation mechanisms. We performed an exhaustive literature search and digitized normalized mass change vs. time data for 407 alloys. To maximize the impact of these and future mass uptake data, we… ▽ More The area-normalized change of mass ($Δ$m/A) with time during the oxidation of metallic alloys is commonly used to assess oxidation resistance. Analyses of such data can also aid in evaluating underlying oxidation mechanisms. We performed an exhaustive literature search and digitized normalized mass change vs. time data for 407 alloys. To maximize the impact of these and future mass uptake data, we developed and published an open, online, computational workflow that fits the data to various models of oxidation kinetics, uses Bayesian statistics for model selection, and makes the raw data and model parameters available via a queryable database. The tool, Refractory Oxidation Database (https://nanohub.org/tools/refoxdb/), uses nanoHUB's Sim2Ls to make the workflow and data (including metadata) findable, accessible, interoperable, and reusable (FAIR). We find that the models selected by the original authors do not match the most likely one according to the Bayesian information criterion (BIC) in 71% of the cases. Further, in 56% of the cases, the published model was not even in the top 3 models according to the BIC. These numbers were obtained assuming an experimental noise of 2.5% of the mass gain range, a smaller noise leads to more discrepancies. The RefOxDB tool is open access and researchers can add their own raw data (those to be included in future publications, as well as negative results) for analysis and to share their work with the community. Such consistent and systematic analysis of open, community generated data can significantly accelerate the development of machine-learning models for oxidation behavior and assist in the understanding and improvement of oxidation resistance. △ Less

Submitted 23 October, 2023; originally announced October 2023.

Preferential Composition during Nucleation and Growth in Multi-Principal Elements Alloys

Authors: Saswat Mishra, Alejandro Strachan

Abstract: The crystallization of complex, concentrated alloys can result in atomic-level short-range order, composition gradients, and phase separation. These features govern the properties of the resulting alloy. While nucleation and growth in single-element metals are well understood, several open questions remain regarding the crystallization of multi-principal component alloys. We use MD to model the cr… ▽ More The crystallization of complex, concentrated alloys can result in atomic-level short-range order, composition gradients, and phase separation. These features govern the properties of the resulting alloy. While nucleation and growth in single-element metals are well understood, several open questions remain regarding the crystallization of multi-principal component alloys. We use MD to model the crystallization of a five-element, equiatomic alloy modeled after CoCrCuFeNi upon cooling from the melt. Stochastic, homogeneous nucleation results in nuclei with a biased composition distribution, rich in Fe and Co. This deviation from the random sampling of the overall composition is driven by the internal energy and affects nuclei of a wide range of sizes, from tens of atoms all the way to super-critical sizes. This results in short range order and compositional gradients at nanometer scales. △ Less

Submitted 23 October, 2023; originally announced October 2023.

GPT-4 as an interface between researchers and computational software: improving usability and reproducibility

Authors: Juan C. Verduzco, Ethan Holbrook, Alejandro Strachan

Abstract: Large language models (LLMs) are playing an increasingly important role in science and engineering. For example, their ability to parse and understand human and computer languages makes them powerful interpreters and their use in applications like code generation are well-documented. We explore the ability of the GPT-4 LLM to ameliorate two major challenges in computational materials science: i) t… ▽ More Large language models (LLMs) are playing an increasingly important role in science and engineering. For example, their ability to parse and understand human and computer languages makes them powerful interpreters and their use in applications like code generation are well-documented. We explore the ability of the GPT-4 LLM to ameliorate two major challenges in computational materials science: i) the high barriers for adoption of scientific software associated with the use of custom input languages, and ii) the poor reproducibility of published results due to insufficient details in the description of simulation methods. We focus on a widely used software for molecular dynamics simulations, the Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS), and quantify the usefulness of input files generated by GPT-4 from task descriptions in English and its ability to generate detailed descriptions of computational tasks from input files. We find that GPT-4 can generate correct and ready-to-use input files for relatively simple tasks and useful starting points for more complex, multi-step simulations. In addition, GPT-4's description of computational tasks from input files can be tuned from a detailed set of step-by-step instructions to a summary description appropriate for publications. Our results show that GPT-4 can reduce the number of routine tasks performed by researchers, accelerate the training of new users, and enhance reproducibility. △ Less

Submitted 4 October, 2023; originally announced October 2023.

Comments: 22 pages, 7 figures

High-Throughput Density Functional Theory Screening of Double Transition Metal MXene Precursors

Authors: Kat Nykiel, Alejandro Strachan

Abstract: MXenes are an emerging class of 2D materials of interest in applications ranging from energy storage to electromagnetic shielding. MXenes are synthesized by selective etching of layered bulk MAX phases into sheets of 2D MXenes. Their chemical tunability has been significantly expanded with the successful synthesis of double transition metal MXenes. While knowledge of the structure and energetics o… ▽ More MXenes are an emerging class of 2D materials of interest in applications ranging from energy storage to electromagnetic shielding. MXenes are synthesized by selective etching of layered bulk MAX phases into sheets of 2D MXenes. Their chemical tunability has been significantly expanded with the successful synthesis of double transition metal MXenes. While knowledge of the structure and energetics of double transition metal MAX phases is critical to designing and optimizing new MXenes, only a small subset of these materials been explored. We present a comprehensive dataset of key properties of MAX phases obtained using density functional theory within the generalized gradient approximation exchange-correlation functionals. Energetics and structure of 8,712 MAX phases have been calculated and stored in a queryable, open database hosted at nanoHUB. △ Less

Submitted 26 July, 2023; originally announced July 2023.

nanoHUB services for FAIR simulations and data: ResultsDB and Sim2Ls

Authors: Daniel Mejia, Steven Clark, Juan Carlos Verduzco, Michael Zentner, Lynn Zentner, Gerhard Klimeck, Alejandro Strachan

Abstract: nanoHUB is an open cyber platform for online simulation, data, and education that seeks to make scientific software and associated data widely available and useful. This paper describes recent developments in our simulation infrastructure to address modern data needs. nanoHUB's Sim2Ls (pronounced sim tools) make simulation, modeling, and data workflows discoverable and accessible to all users for… ▽ More nanoHUB is an open cyber platform for online simulation, data, and education that seeks to make scientific software and associated data widely available and useful. This paper describes recent developments in our simulation infrastructure to address modern data needs. nanoHUB's Sim2Ls (pronounced sim tools) make simulation, modeling, and data workflows discoverable and accessible to all users for cloud computing using standard APIs. In addition, published tools are findable (with digital object identifiers), reusable (via documented requirements and services), and reproducible via containerization. In addition, all Sim2L runs are automatically cached, and their results indexed into a global and queryable database (ResultsDB). We believe this infrastructure significantly lowers the barriers towards making simulation/data workflows and their data findable, accessible, interoperable, and reusable (FAIR). This frictionless access to simulations and data enables researchers, instructors, and students to focus on the application of these products to advance their fields. △ Less

Submitted 19 June, 2023; originally announced June 2023.

Atomistic mechanisms underlying the maximum in diffusivity in doped Li$_7$La$_3$Zr$_2$O$_{12}$

Authors: Juan C. Verduzco, Ernesto E. Marinero, Alejandro Strachan

Abstract: Doped lithium lanthanum zirconium oxide (LLZO) is a promising class of solid electrolytes for lithium-ion batteries due to their good electrochemical stability and compatibility with Li metal anodes. Ionic diffusivity in these ceramics is known to occur via correlated, vacancy mediated, jumps of Li+ between alternating tetrahedral and octahedral sites. Aliovalent doping at the Zr-site increases th… ▽ More Doped lithium lanthanum zirconium oxide (LLZO) is a promising class of solid electrolytes for lithium-ion batteries due to their good electrochemical stability and compatibility with Li metal anodes. Ionic diffusivity in these ceramics is known to occur via correlated, vacancy mediated, jumps of Li+ between alternating tetrahedral and octahedral sites. Aliovalent doping at the Zr-site increases the concentration of vacancies in the Li+ sublattice and cation diffusivity, but such an increase is universally followed by a decrease for Li+ concentration lower than 6.3 - 6.5 Li molar content. Molecular dynamics simulations based on density functional theory show that the maximum in diffusivity originates from competing effects between the increased vacancy concentration and the increasing occupancy of the low-energy tetrahedral sites by Li+, which increases the overall activation energy associated with diffusion. For the relatively high temperatures of our simulations, Li+ concentration plays a dominant role in transport as compared to dopant chemistry. △ Less

Submitted 15 May, 2023; originally announced May 2023.

Mapping microstructure to shock-induced temperature fields using deep learning

Authors: Chunyu Li, Juan Carlos Verduzco, Brian H. Lee, Robert J. Appleton, Alejandro Strachan

Abstract: The response of materials to dynamical, or shock, loading is important to planetary science, aerospace engineering, and energetic materials. Thermal-activated processes, including chemical reactions and phase transitions, are significantly accelerated by the localization of the energy deposited into hotspots. These results from the interaction of a supersonic wave with the materials microstructure… ▽ More The response of materials to dynamical, or shock, loading is important to planetary science, aerospace engineering, and energetic materials. Thermal-activated processes, including chemical reactions and phase transitions, are significantly accelerated by the localization of the energy deposited into hotspots. These results from the interaction of a supersonic wave with the materials microstructure and are governed by complex, coupled processes, including the collapse of porosity, interfacial friction, and localized plastic deformation. These mechanisms are not fully understood and today we lack predictive models to, for example, predict the shock to detonation transition from chemistry and microstructure alone. We demonstrate that deep learning techniques can be used to predict the resulting shock-induced temperature fields in complex composite materials obtained from large-scale molecular dynamics simulations with the initial microstructure as the only input. The accuracy of the Microstructure-Informed Shock-induced Temperature net (MISTnet) model is higher than the current state of the art at a fraction of the computation cost. △ Less

Submitted 30 March, 2023; originally announced March 2023.

Intergranular Hotspots: A Molecular Dynamics Study on the Influence of Compressive and Shear Work

Authors: Brenden W. Hamilton, Matthew P. Kroonblawd, Jalen Macatangay, H. Keo Springer, Alejandro Strachan

Abstract: Numerous crystal- and microstructural-level mechanisms are at play in the formation of hotspots, which are known to govern high explosive initiation behavior. Most of these mechanisms, including pore collapse, interfacial friction, and shear banding, involve both compressive and shear work done within the material and have thus far remained difficult to separate. We assess hotspots formed at shock… ▽ More Numerous crystal- and microstructural-level mechanisms are at play in the formation of hotspots, which are known to govern high explosive initiation behavior. Most of these mechanisms, including pore collapse, interfacial friction, and shear banding, involve both compressive and shear work done within the material and have thus far remained difficult to separate. We assess hotspots formed at shocked crystal-crystal interfaces using quasi-1D molecular dynamics simulations that isolate effects due to compression and shear. Two high explosive materials are considered (TATB and PETN) that exhibit distinctly different levels of molecular conformational flexibility and crystal packing anisotropy. Temperature and intra-molecular strain energy localization in the hotspot is assessed through parametric variation of the crystal orientation and two velocity components that respectively modulate compression and shear work. The resulting hotspots are found to be highly localized to a region within 5-20 nm of the crystal-crystal interface. Compressive work plays a considerably larger role in localizing temperature and intra-molecular strain energy for both materials and all crystal orientations considered. Shear induces a moderate increase in energy localization relative to unsheared cases only for relatively weak compressive shock pressures of approximately 10 GPa. These results help isolate and rank the relative importance of hotspot generation mechanisms and are anticipated to guide the treatment of crystal-crystal interfaces in coarse-grained models of polycrystalline high explosive materials. △ Less

Submitted 21 February, 2023; originally announced February 2023.

High Pressure and Temperature Neural Network Reactive Force Field for Energetic Materials

Authors: Brenden W. Hamilton, Pilsun Yoo, Michael N. Sakano, Md Mahbubul Islam, Alejandro Strachan

Abstract: Reactive force fields for molecular dynamics have enabled a wide range of studies in numerous material classes. These force fields are computationally inexpensive as compared to electronic structure calculations and allow for simulations of millions of atoms. However, the accuracy of traditional force fields is limited by their functional forms, preventing continual refinement and improvement. The… ▽ More Reactive force fields for molecular dynamics have enabled a wide range of studies in numerous material classes. These force fields are computationally inexpensive as compared to electronic structure calculations and allow for simulations of millions of atoms. However, the accuracy of traditional force fields is limited by their functional forms, preventing continual refinement and improvement. Therefore, we develop a neural network based reactive interatomic potential for the prediction of the mechanical, thermal, and chemical response of energetic materials at extreme conditions for energetic materials. The training set is expanded in an automatic iterative approach and consists of various CHNO materials and their reactions under ambient and under shock loading conditions. This new potential shows improved accuracy over the current state of the art force fields for a wide range of properties such as detonation performance, decomposition product formation, and vibrational spectra under ambient and shock loading conditions. △ Less

Submitted 9 February, 2023; originally announced February 2023.

Rapid Activation of Non-Oriented Mechanophores via Shock Loading and Spallation

Authors: Brenden W. Hamilton, Alejandro Strachan

Abstract: Mechanophores, stimuli-responsive molecules that respond chromatically to mechanochemical reactions, are important for understanding the coupling between mechanics and chemistry as well as in engineering applications. However, the atomic-level understanding of their activation originates from gas phase studies or under simple linear elongation forces directly on molecules or polymer chains contain… ▽ More Mechanophores, stimuli-responsive molecules that respond chromatically to mechanochemical reactions, are important for understanding the coupling between mechanics and chemistry as well as in engineering applications. However, the atomic-level understanding of their activation originates from gas phase studies or under simple linear elongation forces directly on molecules or polymer chains containing mechanophores. The effect of many-body distortions, pervasive in condensed-phase applications, is not understood. Therefore, we performed large-scale molecular dynamics simulations of a PMMA-spiropyran co-polymer under dynamic mechanical loading and studied the activation of the mechanophore under various conditions from dynamical compression to tension during unloading. Detailed analysis of the all-atom MD trajectories shows that the mechanophore blocks experience significant many-body intra-molecular distortion that can significantly decrease the activation barrier as compared to when deformation rates are slow relative to molecular relaxation timescales. We find that the reactivity of mechanophores under material compression states is governed by many-body effects of intra-molecular torsions, whereas under tension the reactions are governed by tensile stresses. △ Less

Submitted 29 November, 2022; originally announced November 2022.

Role of electronic thermal transport in amorphous metal recrystallization: a molecular dynamics study

Authors: Zachary D. McClure, Samuel Temple Reeve, Alejandro Strachan

Abstract: Recrystallization of glasses is important in a wide range of applications including electronics and reactive materials. Molecular dynamics (MD) has been used to provide an atomic picture of this process, but prior work has neglected the thermal transport role of electrons, the dominant thermal carrier in metallic systems. We characterize the role of electronic thermal conductivity on the velocity… ▽ More Recrystallization of glasses is important in a wide range of applications including electronics and reactive materials. Molecular dynamics (MD) has been used to provide an atomic picture of this process, but prior work has neglected the thermal transport role of electrons, the dominant thermal carrier in metallic systems. We characterize the role of electronic thermal conductivity on the velocity of recrystallization in Ni using MD coupled to a continuum description of electronic thermal transport via a two-temperature model. Our simulations show that for strong enough coupling between electrons and ions, the increased thermal conductivity removes the heat from the exothermic recrystallization process more efficiently, leading to a lower effective temperature at the recrystallization front and, consequently, lower propagation velocity. We characterize how electron-phonon coupling strength and system size affects front propagation velocity. Interestingly, we find that initial recrystallization velocity increases with decreasing in system size due to higher overall temperatures. Overall, we show that a more accurate description of thermal transport due to the incorporation of electrons results in better agreement with experiments. △ Less

Submitted 2 September, 2022; originally announced September 2022.

Extemporaneous Mechanochemistry: Shockwave Induced Ultrafast Chemical Reactions Due to Intramolecular Strain Energy

Authors: Brenden W. Hamilton, Matthew P. Kroonblawd, Alejandro Strachan

Abstract: Regions of energy localization referred to as hotspots are known to govern shock initiation and the run-to-detonation in energetic materials. Mounting computational evidence points to accelerated chemistry in hotspots from large intramolecular strains induced via the interactions between the shockwave and microstructure. However, definite evidence mapping intramolecular strain to accelerated or al… ▽ More Regions of energy localization referred to as hotspots are known to govern shock initiation and the run-to-detonation in energetic materials. Mounting computational evidence points to accelerated chemistry in hotspots from large intramolecular strains induced via the interactions between the shockwave and microstructure. However, definite evidence mapping intramolecular strain to accelerated or altered chemical reactions has so far been elusive. From a large-scale reactive molecular dynamics simulation of the energetic material TATB, we map molecular temperature and intramolecular strain energy prior to reaction to decomposition kinetics. Both temperature and intramolecular strain are shown to accelerate chemical kinetics. A detailed analysis of the atomistic trajectory shows that intramolecular strain can induce a mechanochemical alteration of decomposition mechanisms. The results in this paper can inform continuum-level chemistry models to account for a wide range of mechanochemical effects. △ Less

Submitted 10 June, 2022; originally announced June 2022.

Community Action on FAIR Data will Fuel a Revolution in Materials Research

Authors: LC Brinson, LM Bartolo, B Blaiszik, D Elbert, I Foster, A Strachan, PW Voorhees

Abstract: Data - arguably the most important product of worldwide materials research investment - are rarely shared. The small and biased proportion of results published are buried in plots and text licensed by journals. This situation wastes resources, hinders innovation, and, in the current era of data-driven discovery, is no longer tenable. In this comment, we identify opportunities for synergistic, coll… ▽ More Data - arguably the most important product of worldwide materials research investment - are rarely shared. The small and biased proportion of results published are buried in plots and text licensed by journals. This situation wastes resources, hinders innovation, and, in the current era of data-driven discovery, is no longer tenable. In this comment, we identify opportunities for synergistic, collaborative, and global actions to assemble large quantities of FAIR (Findable, Accessible, Interoperable, Reusable) (1) materials data. △ Less

Submitted 23 February, 2023; v1 submitted 6 April, 2022; originally announced April 2022.

Deviatoric Stress Driven Transient Melting Below the Glass Transition Temperature in Shocked Polymers

Authors: Jalen Macatangay, Brenden W. Hamilton, Alejandro Strachan

Abstract: The relaxation of polymers around and below their glass transition temperature is governed by a range of correlated unit processes with a wide range of timescales. The fast deformation rates of shock loading can negate a significant fraction of these processes resulting in the dynamical glass transition in rubbers. In this letter we report the inverse, a transient melting of glassy polymer under s… ▽ More The relaxation of polymers around and below their glass transition temperature is governed by a range of correlated unit processes with a wide range of timescales. The fast deformation rates of shock loading can negate a significant fraction of these processes resulting in the dynamical glass transition in rubbers. In this letter we report the inverse, a transient melting of glassy polymer under shock loading. The large deviatoric stresses near the shock front induce fast transitions in backbone dihedral angles and a stress relaxation characteristic of polymer melts. This is followed by the slower relaxation expected for glasses. △ Less

Submitted 5 April, 2022; originally announced April 2022.

Systematic Builder for All-Atom Simulations of Plastically Bonded Explosives

Authors: Chunyu Li, Brenden W Hamilton, Tongtong Shen, Lorena Alzate, Alejandro Strachan

Abstract: The shock to detonation transition in heterogeneous plastically bonded explosives is dominated by energy localization into hotspots that arise from the interaction of the shockwave with microstructural features and defects. The complex polycrystalline structure of these materials leads to a network of hotspot that can coalesce into deflagration and detonation waves. Significant progress has been m… ▽ More The shock to detonation transition in heterogeneous plastically bonded explosives is dominated by energy localization into hotspots that arise from the interaction of the shockwave with microstructural features and defects. The complex polycrystalline structure of these materials leads to a network of hotspot that can coalesce into deflagration and detonation waves. Significant progress has been made on the formation and potency of hotspots using atomistic simulations, but most of the work has focused on ideal and isolated defects. Hence, developed a method, denoted PBXGen, to build realistic PBX microstructures for all-atom simulations. PBXGen is generally applicable, and we demonstrate it with two systems: an RDX-polystyrene PBX with a 3D microstructure and a TATB-polystyrene with columnar grains. The resulting structure exhibit key features of PBXs, albeit at smaller scales, and are validated against experimental mechanical and shock properties. △ Less

Submitted 25 January, 2022; originally announced February 2022.

Active learning and molecular dynamics simulations to find high melting temperature alloys

Authors: David E. Farache, Juan C. Verduzco, Zachary D. McClure, Saaketh Desai, Alejandro Strachan

Abstract: Active learning (AL) can drastically accelerate materials discovery; its power has been shown in various classes of materials and target properties. Prior efforts have used machine learning models for the optimal selection of physical experiments or physics-based simulations. However, the latter efforts have been mostly limited to the use of electronic structure calculations and properties that ca… ▽ More Active learning (AL) can drastically accelerate materials discovery; its power has been shown in various classes of materials and target properties. Prior efforts have used machine learning models for the optimal selection of physical experiments or physics-based simulations. However, the latter efforts have been mostly limited to the use of electronic structure calculations and properties that can be obtained at the unit cell level and with negligible noise. We couple AL with molecular dynamics simulations to identify multiple principal component alloys (MPCAs) with high melting temperatures. Building on cloud computing services through nanoHUB, we present a fully autonomous workflow for the efficient exploration of the high dimensional compositional space of MPCAs. We characterize how uncertainties arising from the stochastic nature of the simulations and the acquisition functions used to select simulations affect the convergence of the approach. Interestingly, we find that relatively short simulations with significant uncertainties can be used to efficiently find the desired alloys as the random forest models used for AL average out fluctuations. △ Less

Submitted 15 October, 2021; originally announced October 2021.

Comments: 23 pages, 11 figures

Sim2Ls: FAIR simulation workflows and data

Authors: Martin Hunt, Steven Clark, Daniel Mejia, Saaketh Desai, Alejandro Strachan

Abstract: Just like the scientific data they generate, simulation workflows for research should be findable, accessible, interoperable, and reusable (FAIR). However, while significant progress has been made towards FAIR data, the majority of science and engineering workflows used in research remain poorly documented and often unavailable, involving ad hoc scripts and manual steps, hindering reproducibility… ▽ More Just like the scientific data they generate, simulation workflows for research should be findable, accessible, interoperable, and reusable (FAIR). However, while significant progress has been made towards FAIR data, the majority of science and engineering workflows used in research remain poorly documented and often unavailable, involving ad hoc scripts and manual steps, hindering reproducibility and stifling progress. We introduce Sim2Ls (pronounced simtools) and the Sim2L Python library that allow developers to create and share end-to-end computational workflows with well-defined and verified inputs and outputs. The Sim2L library makes Sim2Ls, their requirements, and their services discoverable, verifies inputs and outputs, and automatically stores results in a globally-accessible simulation cache and results database. This simulation ecosystem is available in nanoHUB, an open platform that also provides publication services for Sim2Ls, a computational environment for developers and users, and the hardware to execute runs and store results at no cost. We exemplify the use of Sim2Ls using two applications and discuss best practices towards FAIR simulation workflows and associated data. △ Less

Submitted 6 October, 2021; originally announced October 2021.

Comments: 23 pages, 5 figures

The Potential Energy Hotspot: Effects from Impact Velocity, Defect Geometry, and Crystallographic Orientation

Authors: Brenden W. Hamilton, Matthew P. Kroonblawd, Alejandro Strachan

Abstract: In energetic materials, the localization of energy into "hotspots" when a shock wave interacts with the material's microstructure is known to dictate the initiation of chemical reactions and detonation. Recent results have shown that, following the shock-induced collapse of pores with circular cross-sections, more energy is localized as internal potential energy (PE) than can be inferred from the… ▽ More In energetic materials, the localization of energy into "hotspots" when a shock wave interacts with the material's microstructure is known to dictate the initiation of chemical reactions and detonation. Recent results have shown that, following the shock-induced collapse of pores with circular cross-sections, more energy is localized as internal potential energy (PE) than can be inferred from the kinetic energy (KE) distribution. This leads to a complex thermo-mechanical state that is typically overlooked. The mechanisms associated with pore collapse and hotspot formation and the resulting energy localization are known to be highly dependent on material properties, especially its ability to deform plastically and alleviate strain energy, as well as the size and shape of the pore. Therefore, we use molecular dynamics simulations to characterize shock-induced pore collapse and the subsequent formation of hotspots in TATB, a highly anisotropic molecular crystal, for various defect shapes, shock strengths and crystallographic orientations. We find that the the localization of energy as PE is consistently higher and its extent larger than as localized as KE. A detailed analysis of the MD trajectories reveal the underlying molecular process that govern the effect of orientation and pore shape on the resulting hotspots. We find that the regions of highest PE for a given KE relate to not the impact front of the collapse, but the areas of maximum plastic deformation, while KE is maximized at the point of impact. A comparison with previous results in HMX reveal less energy localization in TATB which could be a contributing factor to its insensitivity. △ Less

Submitted 24 September, 2021; originally announced September 2021.

A Hotspots Better Half: Non-Equilibrium Intra-Molecular Strain in Shock Physics

Authors: Brenden W. Hamilton, Matthew P. Kroonblawd, Chunyu Li, Alejandro Strachan

Abstract: Shockwave interactions with material microstructure localizes energy into hotspots, which act as nucleation sites for complex processes such as phase transformations and chemical reactions. To date, hotspots have been described via their temperature fields. Nonreactive, all-atom molecular dynamics simulations of shock-induced pore collapse in a molecular crystal show that more energy is localized… ▽ More Shockwave interactions with material microstructure localizes energy into hotspots, which act as nucleation sites for complex processes such as phase transformations and chemical reactions. To date, hotspots have been described via their temperature fields. Nonreactive, all-atom molecular dynamics simulations of shock-induced pore collapse in a molecular crystal show that more energy is localized as potential energy (PE) than can be inferred from the temperature field and that PE localization persists through thermal diffusion. The origin of the PE hotspot is traced to large intra-molecular strains, storing energy in modes readily available for chemical decomposition. △ Less

Submitted 18 December, 2020; originally announced December 2020.

Comments: Supplemental Material appended to bottom of manuscript document, 19 total pages, 8 total figures, manuscript is 12 pages 4 figures