Page 1 — Guangshuai Han

Guangshuai(Jerry) Han

Guangshuai Han leverages artificial intelligence to drive transformative research across materials and device design. More broadly, he is committed to developing AI-for-science approaches that accelerate innovation across disciplines.

During his PhD, one of his major achievements was pioneering an AI-assisted piezoelectric sensing system that transformed highway infrastructure monitoring. This technology was adopted as a new AASHTO standard, recognized by Time Magazine as one of the Best Inventions of 2023, and successfully transferred into practice as the core technology of a startup company.

He is currently a Postdoctoral Associate at Johns Hopkins University, where his research focuses on applying AI to tackle the extreme complexity of disordered materials systems. Through these approaches, he aims to open new directions for high-entropy disordered materials design and discovery.

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01 AI for Materials Design and Discovery

My research spans multiple directions in AI-driven materials discovery, from chemical composition design and machine-learning force fields to interpretable graph neural networks. The central focus is on high-entropy materials, where I develop physically inspired algorithms to navigate infinite possibilities and identify optimized designs.

Research Sponsors:

National Science Foundation (NSF)
Advanced Research Projects Agency–Energy (ARPA-E)

Github Page:

Thermoelectric Materials Discovery: TE_DIS
XGNN-Piezo: XGNN-Piezo
High-entropy alloy design: HE_FuelCell_Discovery
MLFF with coordination corrected enthalpies: AI-CCE
High-entropy ceramic design: Cyber_cooking
AFLOW materials discovery: aflow++

Publication:

G. Han, W. Hosler, N. Lu, Y. Feng, “An Explainable Framework for Graph Neural Networks in Materials Discovery: A Piezoelectric Case Study”, npj computational materials. (Under Review)
G. Qiu, G. Han, T. Li, X. Xu, S., Tseng, C. Oses, “Accurate Prediction of Metal Iodide Ground States with an AI-Enhanced Correction Framework“, communication materials. (Invited, Under Review)

G. Han, T. Li, X. Xu, J. Lee, G. Qiu, S. Sequeria, A. Ajith, C. Oses, “The search for high-entropy fuel-cell catalysts using disorder descriptors“, Nano Futures. (Invited, Minor Revision)
C. Oses, T. Li, X. Xu, G. Han, G. Qiu, J. Owens, “Beyond the Four Core Effects: Revisiting Thermoelectrics with a High-Entropy Design”, Materials Horizons, 12, 5946-5956, 2025.

G. Han Y. Sun, Y. Feng, G. Lin, N. Lu, ““Artificial intelligence assisted thermoelectric materials design and discovery”, Advanced Electronic Materials, 2300042, 2023. (Featured as cover page)

G. Han, Y.Sun, Y. Feng, G. Lin, N. Lu. “Machine learning regression guided thermoelectric materials discovery– a review” ES Materials and Manufacturing, 14, pp. 20-35, 2021

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