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.
no code implementations • 28 Jul 2023 • Xiangyun Lei, Edward Kim, Viktoriia Baibakova, Shijing Sun
In summary, our study appreciates the benchmark set by these seminal papers while advocating for further enhancements in research reproducibility practices in the field of NLP for materials science.
1 code implementation • 4 Feb 2021 • Xiangyun Lei, Andrew J. Medford
However, the ubiquitous classical force fields cannot describe reactive systems, and quantum molecular dynamics are too computationally demanding to treat large systems or long timescales.
no code implementations • 20 Aug 2019 • Xiangyun Lei, Fred Hohman, Duen Horng Chau, Andrew J. Medford
In recent years, machine learning (ML) has gained significant popularity in the field of chemical informatics and electronic structure theory.