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20182025
most citedAccurate Prediction of Experimental Band Gaps from Large Language Model-Based Data Extraction

8 citations · 12 across the 3 of their papers we have counts for

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Showing cond-mat.mtrl-sciShow all

5 papers · 1 filter

cond-mat.mtrl-sci2025

Tailored ordering enables high-capacity cathode materials

Tzu-chen Liu, Adolfo Salgado-Casanova, So Yubuchi +7

Newly designed Li-ion battery cathode materials with high capacity and greater flexibility in chemical composition will be critical for the growing electric vehicles market. Cathod…

cond-mat.mtrl-sci20241 cited

Generative Hierarchical Materials Search

Sherry Yang, Simon Batzner, Ruiqi Gao +7

Generative models trained at scale can now produce text, video, and more recently, scientific data such as crystal structures. In applications of generative approaches to materials…

cond-mat.mtrl-sci20238 cited

Accurate Prediction of Experimental Band Gaps from Large Language Model-Based Data Extraction

Samuel J. Yang, Shutong Li, Subhashini Venugopalan +5

Machine learning is transforming materials discovery by providing rapid predictions of material properties, which enables large-scale screening for target materials. However, such…

cond-mat.mtrl-sci20213 cited

LiCoO phase stability studied by machine learning-enabled scale bridging between electronic structure, statistical mechanics and phase field theories

Gregory H. Teichert, Sambit Das, Muratahan Aykol +3

LiO (TM={Ni, Co, Mn}) are promising cathodes for Li-ion batteries, whose electrochemical cycling performance is strongly governed by crystal structure and phase stability…

cond-mat.mtrl-sci2018

Network analysis of synthesizable materials discovery

Muratahan Aykol, Vinay I. Hegde, Linda Hung +4

Assessing the synthesizability of inorganic materials is a grand challenge for accelerating their discovery using computations. Synthesis of a material is a complex process that de…