The Role of Reference Points in Machine-Learned Atomistic Simulation Models

no code implementations • 28 Oct 2023 • Xiangyun Lei, Weike Ye, Joseph Montoya, Tim Mueller, Linda Hung, Jens Hummelshoej

This paper introduces the Chemical Environment Modeling Theory (CEMT), a novel, generalized framework designed to overcome the limitations inherent in traditional atom-centered Machine Learning Force Field (MLFF) models, widely used in atomistic simulations of chemical systems.

Learning Properties of Ordered and Disordered Materials from Multi-fidelity Data

3 code implementations • 9 May 2020 • Chi Chen, Yunxing Zuo, Weike Ye, Xiangguo Li, Shyue Ping Ong

Predicting the properties of a material from the arrangement of its atoms is a fundamental goal in materials science.

Materials Science Disordered Systems and Neural Networks

Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals

3 code implementations • Chem. Mater. 2018 • Chi Chen, Weike Ye, Yunxing Zuo, Chen Zheng, Shyue Ping Ong

Similarly, we show that MEGNet models trained on $\sim 60, 000$ crystals in the Materials Project substantially outperform prior ML models in the prediction of the formation energies, band gaps and elastic moduli of crystals, achieving better than DFT accuracy over a much larger data set.

Drug Discovery Formation Energy Materials Science Computational Physics