Research

Intelligent latent representations for structurally complex materials

• Representative papers:
  • Aroboto, B., Chen, S., Hsu, T., Wood, B. C., Jiao, Y., Chapman, J., “Universal and interpretable classification of atomistic structural transitions via unsupervised graph learning“, Applied Physics Letters, September 2023 PDF
  • Chapman, J., Hsu, T., Chen, X., Heo, T. W., Wood, B. C., “Quantifying Disorder One Atom at a Time Using an Interpretable Graph Neural Network Paradigm“, Nature Communications, July 2023 PDF
•  Students
  • Bamidele Aroboto (graduate)

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Disordered materials design 

• Representative papers:
  • Tenorio, M., Md, H. R., Mannodi-Kanakkithodi, A., Chapman, J., “Out-of-distribution machine learning for materials discovery: challenges and opportunities“, Chemical Physics Reviews, March, 2026 PDF
  • Kwon, H., Hsu, T., Sun, W., Jeong, W., Aydin, F., Chapman, J., Chen, X., Lordi, V., Carbone, M., Lu, D., “Spectroscopy-Guided Discovery of Three-Dimensional Structures of Disordered Materials with Diffusion Models“, Machine Learning Science and Technology, October, 2024 PDF
• Students
  • Lucy Sternberg (graduate)
  • Miguel Tenorio (graduate)

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Defect engineering of metal organic framework catalysts

• Representative papers:
• Students
  • Miguel Tenorio (graduate)