activity
20192023
most citedDesign and Analysis of Machine Learning Exchange-Correlation Functionals via Rotationally Invariant Convolutional Descriptors

86 citations · 90 across the 6 of their papers we have counts for

collaborators

6 papers

cond-mat.mtrl-sci2023

Self-consistent convolutional density functional approximations: Application to adsorption at metal surfaces

Sushree Jagriti Sahoo, Qimen Xu, Xiangyun Lei +5

The exchange-correlation (XC) functional in density functional theory is used to approximate multi-electron interactions. A plethora of different functionals is available, but near…

physics.chem-ph2023★ 2 cited

Lessons in Reproducibility: Insights from NLP Studies in Materials Science

Xiangyun Lei, Edward Kim, Viktoriia Baibakova +1

Natural Language Processing (NLP), a cornerstone field within artificial intelligence, has been increasingly utilized in the field of materials science literature. Our study conduc…

physics.chem-ph2023

GMP-Featurizer: A parallelized Python package for efficiently computing the Gaussian Multipole features of atomic systems

Xiangyun Lei, Joseph Montoya

GMP-Featurizer is a lightweight, accurate, efficient, and scalable software package for calculating the Gaussian Multipole (GMP) features \cite{GMP} for a variety of atomic systems…

physics.chem-ph2021★ 1 cited

A Universal Framework for Featurization of Atomistic Systems

Xiangyun Lei, Andrew J. Medford

Molecular dynamics simulations are an invaluable tool in numerous scientific fields. However, the ubiquitous classical force fields cannot describe reactive systems, and quantum mo…

cs.HC2019★ 1 cited

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

Xiangyun Lei, Fred Hohman, Duen Horng Chau +1

In recent years, machine learning (ML) has gained significant popularity in the field of chemical informatics and electronic structure theory. These techniques often require resear…

physics.chem-ph2019★ 86 cited

Design and Analysis of Machine Learning Exchange-Correlation Functionals via Rotationally Invariant Convolutional Descriptors

Xiangyun Lei, Andrew J. Medford

In this work we explore the potential of a new data-driven approach to the design of exchange-correlation (XC) functionals. The approach, inspired by convolutional filters in compu…