End-to-end Symmetry Preserving Inter-atomic Potential Energy Model for Finite and Extended Systems
Linfeng Zhang, Jiequn Han, Han Wang, Wissam Saidi, Roberto Car, Weinan E
Abstract
Machine learning models are changing the paradigm of molecular modeling, which is a fundamental tool for material science, chemistry, and computational biology. Of particular interest is the inter-atomic potential energy surface (PES). Here we develop Deep Potential - Smooth Edition (DeepPot-SE), an end-to-end machine learning-based PES model, which is able to efficiently represent the PES for a wide variety of systems with the accuracy of ab initio quantum mechanics models. By construction, DeepPot-SE is extensive and continuously differentiable, scales linearly with system size, and preserves all the natural symmetries of the system. Further, we show that DeepPot-SE describes finite and extended systems including organic molecules, metals, semiconductors, and insulators with high fidelity.
BibTeX
@inproceedings{NEURIPS2018_e2ad76f2,
author = {Zhang, Linfeng and Han, Jiequn and Wang, Han and Saidi, Wissam and Car, Roberto and E, Weinan},
booktitle = {Advances in Neural Information Processing Systems},
editor = {S. Bengio and H. Wallach and H. Larochelle and K. Grauman and N. Cesa-Bianchi and R. Garnett},
pages = {},
publisher = {Curran Associates, Inc.},
title = {End-to-end Symmetry Preserving Inter-atomic Potential Energy Model for Finite and Extended Systems},
url = {https://proceedings.neurips.cc/paper_files/paper/2018/file/e2ad76f2326fbc6b56a45a56c59fafdb-Paper.pdf},
volume = {31},
year = {2018}
}