Abhijeet Sadashiv Gangan, Ekin Dogus Cubuk, Samuel S Schoenholz, Mathieu Bauchy, N M Anoop Krishnan
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引用次数: 0
Abstract
The accuracy of atomistic simulations depends on the precision of the force fields. Traditional numerical methods often struggle to optimize the empirical force-field parameters for reproducing the target properties. Recent approaches rely on training these force fields based on forces and energies from first-principle simulations. However, it is unclear whether these approaches will enable the capture of complex material responses such as vibrational or elastic properties. To this extent, we introduce a framework employing inner loop simulations and outer loop optimization that exploits automatic differentiation for both property prediction and force-field optimization by computing gradients of the simulation analytically. We demonstrate the approach by optimizing classical potentials such as Stillinger-Weber and EDIP for silicon and BKS for SiO2 to reproduce properties like the elastic constants, vibrational density of states, and phonon dispersion. We also demonstrate how a machine-learned potential can be fine-tuned using automatic differentiation to reproduce any target property such as radial distribution functions. Interestingly, the resulting force field exhibits improved accuracy and generalizability to unseen temperatures compared to those fine-tuned on energies and forces. Finally, we demonstrate the extension of the approach to optimize the force fields toward multiple target properties. Altogether, differentiable simulations, through the analytical computation of their gradients, offer a powerful tool for both theoretical exploration and practical applications toward understanding physical systems and materials.
期刊介绍:
The Journal of Chemical Theory and Computation invites new and original contributions with the understanding that, if accepted, they will not be published elsewhere. Papers reporting new theories, methodology, and/or important applications in quantum electronic structure, molecular dynamics, and statistical mechanics are appropriate for submission to this Journal. Specific topics include advances in or applications of ab initio quantum mechanics, density functional theory, design and properties of new materials, surface science, Monte Carlo simulations, solvation models, QM/MM calculations, biomolecular structure prediction, and molecular dynamics in the broadest sense including gas-phase dynamics, ab initio dynamics, biomolecular dynamics, and protein folding. The Journal does not consider papers that are straightforward applications of known methods including DFT and molecular dynamics. The Journal favors submissions that include advances in theory or methodology with applications to compelling problems.