1 citations · 1 across the 3 of their papers we have counts for
3 papers
Coupling Language Models with Physics-based Simulation for Synthesis of Inorganic Materials
Edward W. Staley, Tom Arbaugh, Michael Pekala +6
Modern generative machine learning (ML) models can propose novel inorganic crystalline materials with targeted properties; however, synthesis planning of these materials remains di…
Self-supervised learning for crystal property prediction via denoising
Alexander New, Nam Q. Le, Michael J. Pekala +1
Accurate prediction of the properties of crystalline materials is crucial for targeted discovery, and this prediction is increasingly done with data-driven models. However, for man…
Evaluating the diversity and utility of materials proposed by generative models
Alexander New, Michael Pekala, Elizabeth A. Pogue +4
Generative machine learning models can use data generated by scientific modeling to create large quantities of novel material structures. Here, we assess how one state-of-the-art g…