SuperDiff: Diffusion Models for Conditional Generation of Hypothetical New Families of Superconductors
This is the implementation of SuperDiff, a state-of-the-art method for computationally generating new hypothetical superconductors. SuperDiff is a new method for generating hypothetical superconductors using Diffusion Models and is the first computational superconductor generation method to generate hypothetical new families of superconductors and also the first to have support for conditioning on reference compounds. This repository contains the code, instructions, and model weights necessary to train or directly run a version of SuperDiff. SuperDiff was created by Samuel Yuan and Sasa Dordevic, you may reach us at sdkyuan@gmail.com and dsasa@uakron.edu.
For ease of replication, pre-trained UNet(s) used for SuperDiff are available in SuperDiff/checkpoints, and outputs contain example raw output data from one experiment with conditional SuperDiff and the four versions of unconditional SuperDiff. Additional output data that support the results of the study are available upon reasonable request to the corresponding author at sdkyuan@gmail.com.
The folder SuperDiff contains notebooks for some of the experiments we conducted. There, diffusion1d-v3-[VERSION]-SAMPLE.ipynb are the unconditional SuperDiff versions (name corresponds to class—cuprates, pnictides, etc.) and diffusion1d_v4_ilvr_YBa1.4Sr0.6Cu3O6Se0.51.ipynb is conditional SuperDiff trained on cuprates conditioned on YBa1.4Sr0.6Cu3O6Se0.51. Code for the other versions of conditional SuperDiff conditioned on various other compounds (including all conditional SuperDiff results presented in the table of generated new families) is available upon reasonable request to the corresponding author at sdkyuan@gmail.com.
If you find this work useful, please cite it as
@misc{yuan2024superdiff,
title={SuperDiff: Diffusion Models for Conditional Generation of Hypothetical New Families of Superconductors},
author={Samuel Yuan and S. V. Dordevic},
year={2024},
eprint={2402.00198},
archivePrefix={arXiv},
primaryClass={cond-mat.supr-con}
}