arXiv subjects

James Goff

Publications and source records attributed to James Goff.

3 recordsLinked to original sources

arXiv2026

Shadow Molecular Dynamics for General Nonlinear Flexible-Charge Models

Machine-learned interatomic potentials have greatly expanded the reach of molecular dynamics simulations, but complex chemical environments often require explicit treatment of long-range electronic response, including charge transfer and polarization. Physics-based flexible-charge models capture these effects but require global nonlinear charge equilibration at every time step to obtain Born-Oppenheimer energies and forces. This process is computationally expensive and can lead to instabilities unless tightly converged. Here, we introduce a shadow molecular dynamics framework for flexible-charge models with arbitrary local nonlinearities and long-range multipolar electrostatics. We replace the exact Born-Oppenheimer potential with a nearby shadow potential obtained by exactly equilibrating a partially linearized electronic energy functional. This provides conservative forces and reduces the global nonlinear equilibration to a quasi-diagonal linear problem that can be solved directly at low cost. Auxiliary dynamical electronic degrees of freedom, propagated through an extended-Lagrangian dynamics, capture the long-range electronic response and define the reference state for the linearization of the energy functional. GPU-accelerated simulations with over 100,000 atoms demonstrate that the shadow potential closely follows the tightly converged Born-Oppenheimer surface, while maintaining long-term stability at a computational cost approaching that of a fixed-charge model. These advances establish an efficient and broadly applicable framework for machine-learned interatomic potentials that explicitly incorporate long-range electrostatic interactions and environment-dependent electronic response.

physics.chem-ph↗

arXiv2023

Shadow molecular dynamics and atomic cluster expansions for flexible charge models

A shadow molecular dynamics scheme for flexible charge models is presented, where the shadow Born-Oppenheimer potential is derived from a coarse-grained approximation of range-separated density functional theory. The interatomic potential, including the atomic electronegativities and the charge-independent short-range part of the potential and force terms, are modeled by the linear atomic cluster expansion (ACE), which provides a computationally efficient alternative to many machine learning methods. The shadow molecular dynamics scheme is based on extended Lagrangian (XL) Born-Oppenheimer molecular dynamics (BOMD) [Eur. Phys. J. B 94, 164 (2021)]. XL-BOMD provides a stable dynamics, while avoiding the costly computational overhead associated with solving an all-to-all system of equations, which normally is required to determine the relaxed electronic ground state prior to each force evaluation. To demonstrate the proposed shadow molecular dynamics scheme for flexible charge models using the atomic cluster expansion, we emulate the dynamics generated from self-consistent charge density functional tight-binding (SCC-DFTB) theory using a second-order charge equilibration (QEq) model. The charge-independent potentials and electronegativities of the QEq model are trained for a supercell of uranium oxide (UO2) and a molecular system of liquid water. The combined ACE + XL-QEq dynamics are stable over a wide range of temperatures both for the oxide and the molecular systems, and provide a precise sampling of the Born-Oppenheimer potential energy surfaces. Accurate ground Coulomb energies are produced by the ACE-based electronegativity model during an NVE simulation of UO2, predicted to be within 1 meV of those from SCC-DFTB on average during comparable simulations.

physics.chem-ph↗

arXiv2023

Exploring Model Complexity in Machine Learned Potentials for Simulated Properties

Machine learning (ML) enables the development of interatomic potentials that promise the accuracy of first principles methods while retaining the low cost and parallel efficiency of empirical potentials. While ML potentials traditionally use atom-centered descriptors as inputs, different models such as linear regression and neural networks can map these descriptors to atomic energies and forces. This begs the question: what is the improvement in accuracy due to model complexity irrespective of choice of descriptors? We curate three datasets to investigate this question in terms of ab initio energy and force errors: (1) solid and liquid silicon, (2) gallium nitride, and (3) the superionic conductor LGPS. We further investigate how these errors affect simulated properties with these models and verify if the improvement in fitting errors corresponds to measurable improvement in property prediction. Since linear and nonlinear regression models have different advantages and disadvantages, the results presented herein help researchers choose models for their particular application. By assessing different models, we observe correlations between fitting quantity (e.g. atomic force) error and simulated property error with respect to ab initio values. Such observations can be repeated by other researchers to determine the level of accuracy, and hence model complexity, needed for their particular systems of interest.

cond-mat.mtrl-sci↗