Advancing molecular simulation with equivariant interatomic potentials
Deep learning has the potential to accelerate atomistic simulations, but existing models
suffer from a lack of robustness, sample efficiency, and accuracy. Simon Batzner, Albert …
suffer from a lack of robustness, sample efficiency, and accuracy. Simon Batzner, Albert …
E (3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials
Abstract This work presents Neural Equivariant Interatomic Potentials (NequIP), an E (3)-
equivariant neural network approach for learning interatomic potentials from ab-initio …
equivariant neural network approach for learning interatomic potentials from ab-initio …
Learning local equivariant representations for large-scale atomistic dynamics
A simultaneously accurate and computationally efficient parametrization of the potential
energy surface of molecules and materials is a long-standing goal in the natural sciences …
energy surface of molecules and materials is a long-standing goal in the natural sciences …
Accelerating atomistic simulations with piecewise machine-learned ab initio potentials at a classical force field-like cost
Recently, machine learning methods have become easy-to-use tools for constructing high-
dimensional interatomic potentials with ab initio accuracy. Although machine-learned …
dimensional interatomic potentials with ab initio accuracy. Although machine-learned …
Universal machine learning for the response of atomistic systems to external fields
Abstract Machine learned interatomic interaction potentials have enabled efficient and
accurate molecular simulations of closed systems. However, external fields, which can …
accurate molecular simulations of closed systems. However, external fields, which can …
Machine learning classical interatomic potentials for molecular dynamics from first-principles training data
The ever-increasing power of modern supercomputers, along with the availability of highly
scalable atomistic simulation codes, has begun to revolutionize predictive modeling of …
scalable atomistic simulation codes, has begun to revolutionize predictive modeling of …
Deep potential molecular dynamics: a scalable model with the accuracy of quantum mechanics
We introduce a scheme for molecular simulations, the deep potential molecular dynamics
(DPMD) method, based on a many-body potential and interatomic forces generated by a …
(DPMD) method, based on a many-body potential and interatomic forces generated by a …
Deep potential: A general representation of a many-body potential energy surface
We present a simple, yet general, end-to-end deep neural network representation of the
potential energy surface for atomic and molecular systems. This methodology, which we call …
potential energy surface for atomic and molecular systems. This methodology, which we call …
The design space of e (3)-equivariant atom-centered interatomic potentials
The rapid progress of machine learning interatomic potentials over the past couple of years
produced a number of new architectures. Particularly notable among these are the Atomic …
produced a number of new architectures. Particularly notable among these are the Atomic …
DPA-1: Pretraining of attention-based deep potential model for molecular simulation
Machine learning assisted modeling of the inter-atomic potential energy surface (PES) is
revolutionizing the field of molecular simulation. With the accumulation of high-quality …
revolutionizing the field of molecular simulation. With the accumulation of high-quality …