Speakers
Description
Room temperature Superconductors are one of the most exciting materials in condensed matter physics, but the hunt for such a material still remains ongoing. The physics of known high temperature superconductors are not well understood and the mechanisms vary and depends on chemical composition and crystal structure. To circumvent the problem of understanding the physics, we instead make use of ML methods to predict whether a material is superconducting and what the critical temperature is. Using the SuperCon [1] dataset which contains 12.000 chemical formulas and their corresponding critical temperature we make use of BDT's to predict properties of materials in the Materials Project [2]. We also attempt to use the crystal structure to improve our predictions using Graph Neural Networks trained on the 3DSC dataset [3].
[1] Materials Database Group. MDR SuperCon Datasheet Ver.220808. https://doi.org/10.48505/nims.3837
[2] A. Jain, S.P. Ong, G. Hautier, W. Chen, W.D. Richards, S. Dacek, S. Cholia, D. Gunter, D. Skinner, G. Ceder, K.A. Persson (*=equal contributions)
The Materials Project: A materials genome approach to accelerating materials innovation
APL Materials, 2013, 1(1), 011002.
doi:10.1063/1.4812323
[3] Sommer, T., Willa, R., Schmalian, J. et al. 3DSC - a dataset of superconductors including crystal structures. Sci Data 10, 816 (2023). https://doi.org/10.1038/s41597-023-02721-y