30 citations · 39 across the 8 of their papers we have counts for
4 papers · 1 filter
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…
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…
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…