Bulletin of the American Physical Society
APS March Meeting 2020
Volume 65, Number 1
Monday–Friday, March 2–6, 2020; Denver, Colorado
Session W43: Computational Design and Discovery of Novel Materials VI: Machine Learning and High Throughput Computing
8:00 AM–11:00 AM,
Friday, March 6, 2020
Room: 702
Sponsoring
Units:
DCOMP DMP
Chair: Yuanxi Wang, Pennsylvania State University
Abstract: W43.00012 : An active-learning framework for the discovery of new crystalline materials*
Presenter:
Kirsten Winther
(Chemical Engineering, Stanford University)
Authors:
Kirsten Winther
(Chemical Engineering, Stanford University)
Raul Flores
(Chemical Engineering, Stanford University)
Christopher Paolucci
(Chemical Engineering, University of Virginia)
Ankit Jain
(Mechanical Engineering, IIT Bombay)
Michal Bajdich
(SUNCAT, SLAC National Accelerator Laboratory)
Thomas bligaard
(Department of Energy, Technical University of Denmark)
Here, we present a ML based active-learning framework to search for stable and meta-stable inorganic materials. It has already been applied to the discovery of new stable polymorphs of IrO, using a search space of experimentally observed crystal prototypes with element substitutions. We extend this search to hypothetical crystal structures that are generated with a crystal prototype enumeration scheme combined with a ML aided search for appropriate lattice parameters. Using a graph-theory based distinction, we identify a finite number of geometries to span a highly diverse material sub-space, which is used for the active-learning exploration.
[1] J. Noh et al. Matter (2019)
[2] L. Ward et al. Physical Review B 96.2 (2017): 024104.
*This work was supported by, and done in collaboration with, the Toyota Research Institute.
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