Inverse Design of Next-Generation Superconductors Using Data-Driven Deep Generative Models

Published

July 18, 2023

Author(s)

Daniel Wines, Tian Xie, Kamal Choudhary

Abstract

Finding new superconductors with a high critical temperature (Tc) has been a challenging task due to computational and experimental costs. We present a diffusion model inspired by the computer vision community to generate new superconductors with unique structures and chemical compositions. Specifically, we used a crystal diffusion variational autoencoder (CDVAE) along with atomistic line graph neural network (ALIGNN) pretrained models and the Joint Automated Repository for Various Integrated Simulations (JARVIS) superconducting database of density functional theory (DFT) calculations to generate new superconductors with a high success rate. We started with a DFT data set of ∼1000 superconducting materials to train the diffusion model. We used the model to generate 3000 new structures, which along with pretrained ALIGNN screening results in 61 candidates. For the top candidates, we performed DFT calculations for validation. Such approaches go beyond funnel-like materials screening approaches and allow for the inverse design of next-generation materials.

Citation

Journal of Physical Chemistry Letters

Pub Type

Journals

Download Paper

https://doi.org/10.1021/acs.jpclett.3c01260

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Citation

Wines, D. , Xie, T. and Choudhary, K. (2023), Inverse Design of Next-Generation Superconductors Using Data-Driven Deep Generative Models, Journal of Physical Chemistry Letters, [online], https://doi.org/10.1021/acs.jpclett.3c01260, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=936647 (Accessed October 15, 2025)