2.8k citations
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Showing 2023 · cond-mat.mtrl-sciShow all
2 papers · 2 filters
cond-mat.mtrl-sci2023★ 10 cited
Towards Foundation Models for Materials Science: The Open MatSci ML Toolkit
Kin Long Kelvin Lee, Carmelo Gonzales, Matthew Spellings +3
Artificial intelligence and machine learning have shown great promise in their ability to accelerate novel materials discovery. As researchers and domain scientists seek to unify a…
cond-mat.mtrl-sci2023★ 112 cited
On the redundancy in large material datasets: efficient and robust learning with less data
Kangming Li, Daniel Persaud, Kamal Choudhary +3
Extensive efforts to gather materials data have largely overlooked potential data redundancy. In this study, we present evidence of a significant degree of redundancy across multip…