Search Results for author: Xiangyun Lei

Found 4 papers, 1 papers with code

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

no code implementations28 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.

Lessons in Reproducibility: Insights from NLP Studies in Materials Science

no code implementations28 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.

Word Embeddings

A Universal Framework for Featurization of Atomistic Systems

1 code implementation4 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.

BIG-bench Machine Learning Computational Efficiency

ElectroLens: Understanding Atomistic Simulations Through Spatially-resolved Visualization of High-dimensional Features

no code implementations20 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.

BIG-bench Machine Learning

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