Shadow molecular dynamics and atomic cluster expansions for flexible charge models

Abstract

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.

References in corpus (28)

• Gaussian Approximation Potentials: the accuracy of quantum mechanics, without the electrons
• Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning
• Quantum-Chemical Insights from Deep Tensor Neural Networks
• A Spectral Analysis Method for Automated Generation of Quantum-Accurate Interatomic Potentials
• A Performance and Cost Assessment of Machine Learning Interatomic Potentials
• Machine learning for molecular simulation
• Big Data meets Quantum Chemistry Approximations: The $\Delta$-Machine Learning Approach
• Machine learning prediction errors better than DFT accuracy
• A Fourth-Generation High-Dimensional Neural Network Potential with Accurate Electrostatics Including Non-local Charge Transfer
• Density-functional tight-binding for beginners
• Interatomic potentials for ionic systems with density functional accuracy based on charge densities obtained by a neural network
• Hierarchical modeling of molecular energies using a deep neural network
• Deep Potential: a general representation of a many-body potential energy surface
• OrbNet: Deep Learning for Quantum Chemistry Using Symmetry-Adapted Atomic-Orbital Features
• Atomic cluster expansion of scalar, vectorial and tensorial properties and including magnetism and charge transfer
• Wavefunction extended Lagrangian Born-Oppenheimer molecular dynamics
• An assessment of the structural resolution of various fingerprints commonly used in machine learning
• Generalized Extended Lagrangian Born-Oppenheimer Molecular Dynamics
• First principles molecular dynamics without self-consistent field optimization
• Next generation extended Lagrangian first principles molecular dynamics
• Analysis of the Time Reversible Born-Oppenheimer Molecular Dynamics
• Mass-zero constrained molecular dynamics for electrode charges in simulations of electrochemical systems
• Adiabatic motion and statistical mechanics via mass zero constrained dynamics
• Inertial Extended-Lagrangian Scheme for Solving Charge Equilibration Models
• Shadow Energy Functionals and Potentials in Born-Oppenheimer Molecular Dynamics
• Mass-Zero constrained dynamics and statistics for the shell model in magnetic field
• Atomic cluster expansion for quantum-accurate large-scale simulations of carbon
• Permutation-adapted complete and independent basis for atomic cluster expansion descriptors