Abstract This work presents Neural Equivariant Interatomic Potentials (NequIP), an E (3)- equivariant neural network approach for learning interatomic potentials from ab-initio …
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 …
Y Zhang, C Hu, B Jiang - Physical Chemistry Chemical Physics, 2021 - pubs.rsc.org
Recently, machine learning methods have become easy-to-use tools for constructing high- dimensional interatomic potentials with ab initio accuracy. Although machine-learned …
Y Zhang, B Jiang - Nature Communications, 2023 - nature.com
Abstract Machine learned interatomic interaction potentials have enabled efficient and accurate molecular simulations of closed systems. However, external fields, which can …
The ever-increasing power of modern supercomputers, along with the availability of highly scalable atomistic simulation codes, has begun to revolutionize predictive modeling of …
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 …
J Han, L Zhang, R Car - arXiv preprint arXiv:1707.01478, 2017 - arxiv.org
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 …
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 …
D Zhang, H Bi, FZ Dai, W Jiang, L Zhang… - arXiv preprint arXiv …, 2022 - arxiv.org
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 …