129 citations · 188 across the 3 of their papers we have counts for
3 papers
Decoding the Stability of Transition-Metal Alloys with Theory-infused Deep Learning
Yang Huang, Shih-Han Wang, Shuyi Cao +2
We introduce an interpretable deep learning framework that predicts the cohesive energy of transition-metal alloys (TMAs) by embedding cohesion theory within graph neural networks…
JARVIS-Leaderboard: A Large Scale Benchmark of Materials Design Methods
Kamal Choudhary, Daniel Wines, Kangming Li +35
Lack of rigorous reproducibility and validation are major hurdles for scientific development across many fields. Materials science in particular encompasses a variety of experiment…
Infusing Theory into Deep Learning for Interpretable Reactivity Prediction
Shih-Han Wang, Hemanth Somarajan Pillai, Siwen Wang +2
Despite recent advances of data acquisition and algorithms development, machine learning (ML) faces tremendous challenges to being adopted in practical catalyst design, largely due…