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1. End-to-End Differentiable Reactive Molecular Dynamics Simulations Using JAX

   https://doi.org/10.1007/978-3-031-32041-5_11  

   Kaymak, M.C.; Schoenholz, S.S.; Cubuk, E.D.; O’Hearn, K.A.; Merz Jr., K.M.; Aktulga, H.M. (May 2023, Lecture notes in computer science)

   Bhatele, A.; Hammond, J.; Baboulin, M.; Kruse, C. (Ed.)

   The reactive force field (ReaxFF) interatomic potential is a powerful tool for simulating the behavior of molecules in a wide range of chemical and physical systems at the atomic level. Unlike traditional classical force fields, ReaxFF employs dynamic bonding and polarizability to enable the study of reactive systems. Over the past couple decades, highly optimized parallel implementations have been developed for ReaxFF to efficiently utilize modern hardware such as multi-core processors and graphics processing units (GPUs). However, the complexity of the ReaxFF potential poses challenges in terms of portability to new architectures (AMD and Intel GPUs, RISC-V processors, etc.), and limits the ability of computational scientists to tailor its functional form to their target systems. In this regard, the convergence of cyber-infrastructure for high performance computing (HPC) and machine learning (ML) presents new opportunities for customization, programmer productivity and performance portability. In this paper, we explore the benefits and limitations of JAX, a modern ML library in Python representing a prime example of the convergence of HPC and ML software, for implementing ReaxFF. We demonstrate that by leveraging auto-differentiation, just-in-time compilation, and vectorization capabilities of JAX, one can attain a portable, performant, and easy to maintain ReaxFF software. Beyond enabling MD simulations, end-to-end differentiability of trajectories produced by ReaxFF implemented with JAX makes it possible to perform related tasks such as force field parameter optimization and meta-analysis without requiring any significant software developments. We also discuss scalability limitations using the current version of JAX for ReaxFF simulations. 

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2. Active learning of reactive Bayesian force fields applied to heterogeneous catalysis dynamics of H/Pt

   https://doi.org/10.1038/s41467-022-32294-0  

   Vandermause, Jonathan; Xie, Yu; Lim, Jin Soo; Owen, Cameron J.; Kozinsky, Boris (September 2022, Nature Communications)

   Abstract Atomistic modeling of chemically reactive systems has so far relied on either expensive ab initio methods or bond-order force fields requiring arduous parametrization. Here, we describe a Bayesian active learning framework for autonomous “on-the-fly” training of fast and accurate reactive many-body force fields during molecular dynamics simulations. At each time-step, predictive uncertainties of a sparse Gaussian process are evaluated to automatically determine whether additional ab initio training data are needed. We introduce a general method for mapping trained kernel models onto equivalent polynomial models whose prediction cost is much lower and independent of the training set size. As a demonstration, we perform direct two-phase simulations of heterogeneous H₂turnover on the Pt(111) catalyst surface at chemical accuracy. The model trains itself in three days and performs at twice the speed of a ReaxFF model, while maintaining much higher fidelity to DFT and excellent agreement with experiment. 

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3. Scalar Auxiliary Variable (SAV) Stabilization of Implicit‐Explicit (IMEX) Time Integration Schemes for Non‐Linear Structural Dynamics

   https://doi.org/10.1002/nme.7660  

   Kwon, Sun‐Beom; Prakash, Arun (January 2025, International Journal for Numerical Methods in Engineering)

   Implicit-explicit (IMEX) time integration schemes are well suited for non-linear structural dynamics because of their low computational cost and high accuracy. However, the stability of IMEX schemes cannot be guaranteed for general non-linear problems. In this article, we present a scalar auxiliary variable (SAV) stabilization of high-order IMEX time integration schemes that leads to unconditional stability. The proposed IMEX-BDFk-SAV schemes treat linear terms implicitly using kth-order backward difference formulas (BDFk) and non-linear terms explicitly. This eliminates the need for iterations in non-linear problems and leads to low computational costs. Truncation error analysis of the proposed IMEX-BDFk-SAV schemes confirms that up to kth-order accuracy can be achieved and this is verified through a series of convergence tests. Unlike existing SAV schemes for first-order ordinary differential equations (ODEs), we introduce a novel SAV for the proposed schemes that allows direct solution of the second-order ODEs without transforming them into a system of first-order ODEs. Finally, we demonstrate the performance of the proposed schemes by solving several non-linear problems in structural dynamics and show that the proposed schemes can achieve high accuracy at a low computational cost while maintaining unconditional stability. 

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4. Modeling for Structural Engineering and Synthesis of Two Dimensional WSe2 Using a Newly Developed ReaxFF Reactive Force Field

   Nayir, Nadire; Wang, Yuanxi; Shabnam, Sharmin; Hickey, Danielle Reifsnyder; Miao, Leixin; Zhang, Xiaotian; Bachu, Saiphaneendra; Alem, Nasim; Redwing, Joan; Crespi, Vincent H.; et al (December 2020, Journal of physical chemistry)

   null (Ed.)

   Atomistic simulation techniques have become an indispensable tool to acquire a fundamental understanding of growth and structural characteristics of two-dimensional (2D) materials of interest, thereby accelerating experimental research in the same field. A new ReaxFF reactive force field presented here is the first comprehensive empirical potential that is explicitly designed to capture the most prominent features of 2D WSe2 solid-phase chemistry, such as defect formation as a function of local geometry and chalcogen chemical potential, vacancy migration and phase transition, thus enabling cost-effective and reliable characterization of 2D WSe2 at large length scales and time scales much longer than what is accessible by first-principles theory. This potential, validated using extensive first-principles energetics data on both periodic and nonperiodic systems and experimental measurements, can accurately describe the mechanochemical coupling between monolayer deformations and vacancy energetics, providing valuable atomistic insights into the morphological evolution of a monolayer in different environments in terms of loading conditions and various concentrations and distributions of defects. Since understanding how growth is affected by the local chemical environment is vital to fabricating efficient and functional atomically thin 2D WSe2, the new ReaxFF description enables investigations of edge-controlled growth of single crystals of 2D WSe2 using reactive environments closely matching experimental conditions at a low computational cost. 

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5. Efficient exploration of reaction pathways using reaction databases and active learning

   https://doi.org/10.1063/5.0235715  

   Kuryla, Domantas; Csányi, Gábor; van_Duin, Adri_C T; Michaelides, Angelos (March 2025, The Journal of Chemical Physics)

   Lian, T (Ed.)

   The fast and accurate simulation of chemical reactions is a major goal of computational chemistry. Recently, the pursuit of this goal has been aided by machine learning interatomic potentials (MLIPs), which provide energies and forces at quantum mechanical accuracy but at a fraction of the cost of the reference quantum mechanical calculations. Assembling the training set of relevant configurations is key to building the MLIP. Here, we demonstrate two approaches to training reactive MLIPs based on reaction pathway information. One approach exploits reaction datasets containing reactant, product, and transition state structures. Using an SN2 reaction dataset, we accurately locate reaction pathways and transition state geometries of up to 170 unseen reactions. In another approach, which does not depend on data availability, we present an efficient active learning procedure that yields an accurate MLIP and converged minimum energy path given only the reaction end point structures, avoiding quantum mechanics driven reaction pathway search at any stage of training set construction. We demonstrate this procedure on an SN2 reaction in the gas phase and with a small number of solvating water molecules, predicting reaction barriers within 20 meV of the reference quantum chemistry method. We then apply the active learning procedure on a more complex reaction involving a nucleophilic aromatic substitution and proton transfer, comparing the results against the reactive ReaxFF force field. Our active learning procedure, in addition to rapidly finding reaction paths for individual reactions, provides an approach to building large reaction path databases for training transferable reactive machine learning potentials. 

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