Inverse Design of Next-generation Superconductors Using Data-driven Deep Generative Models

Abstract

Finding new superconductors with a high critical temperature ($T_c$) 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 dataset of $\approx$1000 superconducting materials to train the diffusion model. We used the model to generate 3000 new structures, which along with pre-trained ALIGNN screening results in 61 candidates. For the top candidates, we performed DFT calculations for validation. Such approaches go beyond the funnel-like materials design approaches and allow for the inverse design of next-generation materials.

References in corpus (5)

• Quantum ESPRESSO: a modular and open-source software project for quantum simulations of materials
• Restoring the density-gradient expansion for exchange in solids and surfaces
• Generalized gradient approximation for solids and their surfaces
• High-throughput search for magnetic topological materials using spin-orbit spillage, machine-learning and experiments
• Superconducting TiN Films Sputtered over a Large Range of Substrate DC Bias

Cited by in corpus (9)

• AI-driven inverse design of materials: Past, present and future
• Recent progress in the JARVIS infrastructure for next-generation data-driven materials design
• A Self-Improvable Polymer Discovery Framework Based on Conditional Generative Model
• InvDesFlow: An AI-driven materials inverse design workflow to explore possible high-temperature superconductors
• Data-driven Design of High Pressure Hydride Superconductors using DFT and Deep Learning
• The JARVIS Infrastructure is All You Need for Materials Design
• Global optimization in the discrete and variable-dimension conformational space: The case of crystal with the strongest atomic cohesion
• Charting the landscape of Bardeen-Cooper-Schrieffer superconductors in experimentally known compounds
• Discovery of 2D Materials via Symmetry-Constrained Diffusion Model