materials-discovery.zip

This contains files and data needed to reproduce the machine learning results of the paper "Materials discovery using generative machine learning". Further information can be found at: https://github.com/newalexander/generative-materials-discoveryThe <b>data</b> folder contains:<b>alignn/Ed_tern.csv</b>: The materials project (MP) IDs and decomposition enthalpies of material structures. The decomposition enthalpies were obtained from the work of Bartel et al. (2020).<b>pgcgm/Ed_tern.csv</b>: The predicted decomposition enthalpies of the structures generated by the PGCGM (Zhao et al., 2023).<b>pgcgm/cifs.zip</b>: The CIFs for the structured generated by the PGCGM.The <b>Ed_tern</b> folder contains the trained ALIGNN (Choudhary et al., 2021) model we used to evaluate on PGCGM data.ReferencesBartel, C. J., Trewartha, A., Wang, Q., Dunn, A., Jain, A., and Ceder, G. A critical examination of com- pound stability predictions from machine-learned for- mation energies. npj Computational Materials, 6(1): 97, Jul 2020. ISSN 2057-3960. doi: 10.1038/ s41524-020-00362-y. URL https://doi.org/10. 1038/s41524-020-00362-yChoudhary, K. and DeCost, B. Atomistic line graph neural network for improved materials property pre- dictions. npj Computational Materials, 7(1):185, Nov 2021. ISSN 2057-3960. doi: 10.1038/ s41524-021-00650-1. URL https://doi.org/10. 1038/s41524-021-00650-1Zhao, Y., Siriwardane, E. M. D., Wu, Z., Fu, N., Al- Fahdi, M., Hu, M., and Hu, J. Physics guided deep learning for generative design of crystal materials with symmetry constraints. npj Computational Materials, 9 (1):38, Mar 2023. ISSN 2057-3960. doi: 10.1038/ s41524-023-00987-9. URL https://doi.org/10. 1038/s41524-023-00987-9<br>