Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals

• Chi Chen  |  Logan Ward  |  Shyue Ping Ong

• Battaglia, P. W.; Hamrick, J. B.; Bapst, V.; Sanchez-Gonzalez, A.; Zambaldi, V.; Malinowski, M.; Tacchetti, A.; Raposo, D.; Santoro, A.; Faulkner, R.; et al. Relational inductive biases, deep learning, and graph networks. 2018, 1–38. arXiv:1806.01261  |  Chen, C.; Ye, W.; Zuo, Y.; Zheng, C.; Ong, S. P. Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals. Chemistry of Materials 2019, 31(9), 3564-3572. doi:10.1021/acs.chemmater.9b01294  |  Ong, S. P.; Cholia, S.; Jain, A.; Brafman, M.; Gunter, D.; Ceder, G.; Persson, K. A. The Materials Application Programming Interface (API): A simple, flexible and efficient API for materials data based on REpresentational State Transfer (REST) princi  |  Vinyals, O.; Bengio, S.; Kudlur, M. Order Matters: Sequence to sequence for sets. 2015, arXiv preprint. arXiv:1511.06391

The MatErials Graph Network (MEGNet) is an implementation of DeepMind's graph networks[1] for universal machine learning in materials science. We have demonstrated its success in achieving very low prediction errors in a broad array of properties in both molecules and crystals (see "Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals"[2]). New releases have included our recent work on multi-fidelity materials property modeling (See "Learning properties of ordered and disordered materials from multi-fidelity data"[3]).

Input variables : QM9 data set
Output Variables : QM9 molecule data: HOMO: Highest occupied molecular orbital energy LUMO: Lowest unoccupied molecular orbital energy Gap: energy gap ZPVE: zero point vibrational energy µ: dipole moment α: isotropic polarizability R2: electronic spatial extent U0: internal energy at 0 K U: internal energy at 298 K H: enthalpy at 298 K G: Gibbs free energy at 298 K Cv: heat capacity at 298 K ω1: highest vibrational frequency. Materials Project data: Formation energy from the elements Band gap Log

Visit Model : arxiv.org

Additional links : figshare.com