Bulletin of the American Physical Society
2024 APS March Meeting
Monday–Friday, March 4–8, 2024; Minneapolis & Virtual
Session Q43: Emergent Topics in Machine Learning for Molecular Systems and Materials
3:00 PM–6:00 PM,
Wednesday, March 6, 2024
Room: Auditorium 1
Sponsoring
Unit:
GDS
Chair: Talid Sinno, University of Pennsylvania
Abstract: Q43.00003 : Self-assembly of electronic materials and the power of machine learning*
4:12 PM–4:48 PM
Presenter:
Paulette Clancy
(The Johns Hopkins University)
Author:
Paulette Clancy
(The Johns Hopkins University)
*This work has been primarily supported by the U. S. Depart- ment of Energy (DOE), Office of Science, Basic Energy Sciences (BES), under Award #DE-SC0022305 (formulation engineering of energy materials via multiscale learning spirals). This work has also been supported by National Science Foundation (NSF) grant #2107360 and the HEMI Seed Grant. Computing resources were provided by the Advanced Research Computing at Hop- kins (ARCH) high-performance computing (HPC) facilities, which is supported by National Science Foundation (NSF) grant number OAC 1920103.
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