MOF: creating an educational game on nanotechnology through simulation-driven optimization. - Tiannan Chen; Xiangyun Lei; Hakan Demir; Christopher J. Cramer; Laura Gagliardi; Stephen J. Guy (MIG, 2016)

ElectroLens: Understanding Atomistic Simulations through Spatially-Resolved Visualization of High-Dimensional Features. - Xiangyun Lei; Fred Hohman; Duen Horng (Polo) Chau; Andrew J. Medford (IEEE VIS (Short Papers), 2019)

A Universal Framework for Featurization of Atomistic Systems. - Xiangyun Lei; Andrew J. Medford (CoRR, 2021)

A Unified Framework for Data-Driven Atomistic Modeling. - Xiangyun Lei (2022)

Lessons in Reproducibility: Insights from NLP Studies in Materials Science. - Xiangyun Lei; Edward Kim; Viktoriia Baibakova; Shijing Sun (CoRR, 2023)

The Role of Reference Points in Machine-Learned Atomistic Simulation Models. - Xiangyun Lei; Weike Ye; Joseph H. Montoya; Timothy Mueller; Linda Hung; Jens S. Hummelshoej (CoRR, 2023)

AmpTorch: A Python package for scalable fingerprint-based neural network training on multi-element systems with integrated uncertainty quantification. - Muhammed Shuaibi; Yuge Hu; Xiangyun Lei; Benjamin M. Comer; Matt Adams; Jacob Paras; Rui Qi Chen; Eric Musa; Joseph Musielewicz; Andrew A. Peterson; Andrew J. Medford; Zachary W. Ulissi (J. Open Source Softw., 2023)

GMP-Featurizer: A parallelized Python package for efficiently computing the Gaussian Multipole features of atomic systems. - Xiangyun Lei; Joseph H. Montoya (J. Open Source Softw., 2023)