214 citations · 1k across the 35 of their papers we have counts for
5 papers · 1 filter
AtomGPT: Atomistic Generative Pre-trained Transformer for Forward and Inverse Materials Design
Kamal Choudhary
Large language models (LLMs) such as generative pretrained transformers (GPTs) have shown potential for various commercial applications, but their applicability for materials desig…
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,…
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…
Developments and applications of the OPTIMADE API for materials discovery, design, and data exchange
Matthew L. Evans, Johan Bergsma, Andrius Merkys +56
The Open Databases Integration for Materials Design (OPTIMADE) application programming interface (API) empowers users with holistic access to a growing federation of databases, enh…
InterMat: Accelerating Band Offset Prediction in Semiconductor Interfaces with DFT and Deep Learning
Kamal Choudhary, Kevin Garrity
We introduce a computational framework (InterMat) to predict band offsets of semiconductor interfaces using density functional theory (DFT) and graph neural networks (GNN). As a fi…