most citedCheMatAgent: Enhancing LLMs for Chemistry and Materials Science through Tree-Search Based Tool Learning

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cond-mat.mtrl-sci2026

Predicting Novel Stable Materials for Experimental Synthesis

Yuqi An, Sihong Zhu, Joseph Montoya +2

Machine-learning-accelerated materials discovery has yielded large numbers of computationally stable compounds, yet many remain experimentally unrealized, underscoring a persistent…

cond-mat.mtrl-sci2026★ 1 cited

Accelerating Complex Materials Discovery with Universal Machine-Learning Potential-Driven Structure Prediction

Yuqi An, Zhenbin Wang

Universal machine-learning interatomic potentials (uMLIPs) have become powerful tools for accelerating computational materials discovery by replacing expensive first-principles cal…