390 citations · 578 across the 4 of their papers we have counts for
4 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…
Federated Learning Enables Big Data for Rare Cancer Boundary Detection
Sarthak Pati, Ujjwal Baid, Brandon Edwards +276
Although machine learning (ML) has shown promise in numerous domains, there are concerns about generalizability to out-of-sample data. This is currently addressed by centrally shar…
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…