30 citations · 39 across the 8 of their papers we have counts for
8 papers
Human and LLM Collaboration for Accelerated Materials Synthesis and Discovery
Gregory Bassen, Wyatt Bunstine, Sarah Okandey +7
Although Large Language Models (LLM) and Artificial Intelligence (AI) tools have enabled a rapid increase in the generation rate of predicted materials, the rate of new materials d…
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…
Deep Learning Accelerated Phase Prediction of Refractory Multi-Principal Element Alloys
A. K. Shargh, C. D. Stiles, J. A. El-Awady
The tunability of the mechanical properties of refractory multi-principal-element alloys (RMPEAs) make them attractive for numerous high-temperature applications. It is well-establ…
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…
Closed-loop machine learning for discovery of novel superconductors
Elizabeth A. Pogue, Alexander New, Kyle McElroy +15
The discovery of novel materials drives industrial innovation, although the pace of discovery tends to be slow due to the infrequency of "Eureka!" moments. These moments are typica…
A Statistical Perspective for Predicting the Strength of Metals: Revisiting the Hall-Petch Relationship using Machine Learning
Yejun Gu, Christopher D. Stiles, Jaafar A. El-Awady
The mechanical properties of a material are intimately related to its microstructure. This is particularly important for predicting mechanical behavior of polycrystalline metals, w…
Curvature-informed multi-task learning for graph networks
Alexander New, Michael J. Pekala, Nam Q. Le +3
Properties of interest for crystals and molecules, such as band gap, elasticity, and solubility, are generally related to each other: they are governed by the same underlying laws…