Previous efforts to understand structure‐function relationships in high ionic conductivity materials for solid state batteries have predominantly relied on density functional theory (DFT‐) based ab initio molecular dynamics (MD). Such simulations, however, are computationally demanding and cannot be reasonably applied to large systems containing more than a hundred atoms. Here, an artificial neural network (ANN) is trained to accelerate the calculation of high accuracy atomic forces and energies used during such MD simulations. After carefully training a robust ANN for four and five element systems, nearly identical lithium ion diffusivities are obtained for Li10GeP2S12(LGPS) when benchmarking the ANN‐MD results with DFT‐MD. Applying the ANN‐MD approach, the effect of chlorine doping on the lithium diffusivity is calculated in an LGPS‐like structure and it is found that a dopant concentration of 1.3% maximizes ionic conductivity. The optimal concentration balances the competing consequences of effective atomic radii and dielectric constants on lithium diffusion and agrees with the experimental composition. Performing simulations at the resolution necessary to model experimentally relevant and optimal concentrations would be infeasible with traditional DFT‐MD. Systems that require a large number of simulated atoms can be studied more efficiently while maintaining high accuracy with the proposed ANN‐MD framework.
Development and validation of ReaxFF forcefield for solid-state ion conduction in Ag/S-based systems.
Abstract: Solid-state ion conduction (SSIC) is a mechanism of ionic current that has garnered increasing attention for applications in all-solid-state batteries and atomic switches. The Ag/S SSIC system in β-Ag S, possessing the highest ionic conductivity of any known material, provides a unique opportunity to better understand the fundamental nature of SSIC. β-Ag S is topographically similar to binary perovskites except that
it is cubic, leading to isotropic SSIC exceeding 4 S/cm. The dynamic nature of SSIC makes it difficult to study by observational means, where inherent time-averaging obscures correlations among atomic transit routes.Molecular dynamics (MD) is a tool ideally suited for gaining insight into large atomic systems with subnanosecond
time resolutions. However, traditional MD potentials lack a description of bond-breaking/forming reactions, which are an essential aspect of SSIC and related memristic properties. This limitation can be overcome by using a reactive force field (ReaxFF), which enables the simulation of bonding reactions with DFT-level accuracy.
In this study, we present a ReaxFF force field for the Ag/S system, optimized for simulating SSIC in β-Ag S.
Training data consisted of crystal structures, Bader partial charges, and energies of various Ag/S clusters calculated at the DFT-level. Energies were obtained with Gaussian 16, using the PBEh1PBE hybrid functional with a triple-zeta correlation-consistent basis set. Multiobjective parameter optimization was accomplished with an updated form of the Genetic Algorithm for Reactive Force Fields (GARFfield). The force field was validated
with potential energy and ion conductivity calculations, along with relevant structural features. Results were compared with equivalent simulations from other established potentials. This new ReaxFF force field will enable modeling of realistic SSIC configurations for Ag/S-based materials and provides a viable approach for
extending ReaxFF to other SSIC systems in the future. This work was supported by the National Science Foundation under grant #2025319.
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- Award ID(s):
- 2025319
- PAR ID:
- 10250786
- Date Published:
- Journal Name:
- National Meeting of the American Chemical Society
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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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.more » « less
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