APS Logo

ML-Driven Discovery: Accelerating Real-World Impacts in Physics and Materials Science

INVITED · MAR-A10 · ID: 2763203

Presentations

  • Accelerating Materials Discovery with HPC and AI

    ORAL · Invited

    –

    Publication: 1. Chen, C. and Ong, S.P., 2022. A universal graph deep learning interatomic potential for the periodic table. Nature Computational Science, 2(11), pp.718-728.<br>2. Chen, C., Nguyen, D.T., Lee, S.J., Baker, N.A., Karakoti, A.S., Lauw, L., Owen, C., Mueller, K.T., Bilodeau, B.A., Murugesan, V. and Troyer, M., 2024. Accelerating Computational Materials Discovery with Machine Learning and Cloud High-Performance Computing: from Large-Scale Screening to Experimental Validation. Journal of the American Chemical Society, 146(29), pp.20009-20018.

    Presenters

    • Chi Chen

      Microsoft

    Authors

    • Chi Chen

      Microsoft

    View abstract →