Evaluating the diversity and utility of materials proposed by generative models

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 generative model, the physics-guided crystal generation model (PGCGM), can be used as part of the inverse design process....

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Bibliographic Details
Main Authors New, Alexander, Pekala, Michael, Pogue, Elizabeth A, Le, Nam Q, Domenico, Janna, Piatko, Christine D, Stiles, Christopher D
Format Journal Article
LanguageEnglish
Published 09.08.2023
Subjects
Computer Science - Learning
Physics - Computational Physics
Physics - Materials Science
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Summary: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 generative model, the physics-guided crystal generation model (PGCGM), can be used as part of the inverse design process. We show that the default PGCGM's input space is not smooth with respect to parameter variation, making material optimization difficult and limited. We also demonstrate that most generated structures are predicted to be thermodynamically unstable by a separate property-prediction model, partially due to out-of-domain data challenges. Our findings suggest how generative models might be improved to enable better inverse design.
DOI:10.48550/arxiv.2309.12323