549 citations · 1.7k across the 44 of their papers we have counts for
10 papers · 1 filter
LLM4Mat-Bench: Benchmarking Large Language Models for Materials Property Prediction
Andre Niyongabo Rubungo, Kangming Li, Jason Hattrick-Simpers +1
Large language models (LLMs) are increasingly being used in materials science. However, little attention has been given to benchmarking and standardized evaluation for LLM-based ma…
Open Catalyst Experiments 2024 (OCx24): Bridging Experiments and Computational Models
Jehad Abed, Jiheon Kim, Muhammed Shuaibi +17
The search for low-cost, durable, and effective catalysts is essential for green hydrogen production and carbon dioxide upcycling to help in the mitigation of climate change. Disco…
Evaluating the Performance and Robustness of LLMs in Materials Science Q&A and Property Predictions
Hongchen Wang, Kangming Li, Scott Ramsay +3
Large Language Models (LLMs) have the potential to revolutionize scientific research, yet their robustness and reliability in domain-specific applications remain insufficiently exp…
An Assessment of Commonly Used Equivalent Circuit Models for Corrosion Analysis: A Bayesian Approach to Electrochemical Impedance Spectroscopy
Runze Zhang, Debashish Sur, Kangming Li +5
Electrochemical Impedance Spectroscopy (EIS) is a crucial technique for assessing corrosion of a metallic materials. The analysis of EIS hinges on the selection of an appropriate e…
Probing out-of-distribution generalization in machine learning for materials
Kangming Li, Andre Niyongabo Rubungo, Xiangyun Lei +5
Scientific machine learning (ML) endeavors to develop generalizable models with broad applicability. However, the assessment of generalizability is often based on heuristics. Here,…
High-Tc superconductor candidates proposed by machine learning
Siwoo Lee, Jason Hattrick-Simpers, Young-June Kim +1
We cast the relation between the chemical composition of a solid-state material and its superconducting critical temperature (Tc) as a statistical learning problem with reduced com…
Efficient first principles based modeling via machine learning: from simple representations to high entropy materials
Kangming Li, Kamal Choudhary, Brian DeCost +2
High-entropy materials (HEMs) have recently emerged as a significant category of materials, offering highly tunable properties. However, the scarcity of HEM data in existing densit…
Artificial Intelligence-Enabled Optimization of Battery-Grade Lithium Carbonate Production
S. Shayan Mousavi Masouleh, Corey A. Sanz, Ryan P. Jansonius +4
By 2035, the need for battery-grade lithium is expected to quadruple. About half of this lithium is currently sourced from brines and must be converted from a chloride into lithium…
Accurate and efficient predictions of keyhole dynamics in laser materials processing using machine learning-aided simulations
Jiahui Zhang, Runbo Jiang, Kangming Li +11
The keyhole phenomenon has been widely observed in laser materials processing, including laser welding, remelting, cladding, drilling, and additive manufacturing. Keyhole-induced d…
Roadmap on Data-Centric Materials Science
Stefan Bauer, Peter Benner, Tristan Bereau +58
Science is and always has been based on data, but the terms "data-centric" and the "4th paradigm of" materials research indicate a radical change in how information is retrieved, h…