Bulletin of the American Physical Society
APS March Meeting 2023
Volume 68, Number 3
Las Vegas, Nevada (March 5-10)
Virtual (March 20-22); Time Zone: Pacific Time
Session N62: Quantum Embedding: Methods and Models
11:30 AM–2:30 PM,
Wednesday, March 8, 2023
Room: Room 417
Sponsoring
Unit:
DCOMP
Chair: Cyrus Dreyer, Stony Brook University (SUNY)
Abstract: N62.00004 : Learning emergent models from ab initio many-body calculations*
12:54 PM–1:06 PM
Presenter:
Yueqing Chang
(Rutgers, The State University of New Jersey)
Authors:
Yueqing Chang
(Rutgers, The State University of New Jersey)
Lucas K Wagner
(University of Illinois at Urbana-Champaign)
In this study, we applied real-space variational quantum Monte Carlo to compute the many-body eigenstate wave functions for hydrogen chains. We then used unsupervised machine learning to cluster the ab initio many-body eigenstates based on various descriptors. The emergent spin degree of freedom described by the antiferromagnetic Heisenberg model was explicitly identified using the clustering model at large bond lengths. We highlight that the clustering of the ab initio eigenstates is not based on a pre-selected energy cutoff but learned using descriptors including total energy, spin-spin correlations, and double occupancies.
*Y.C. was supported by the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences, Computational Materials Sciences Program, under Award No. DE-SC0020177. This research used resources of the Oak Ridge Leadership Computing Facility at the Oak Ridge National Laboratory, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC05-00OR22725. This work made use of the Illinois Campus Cluster, a computing resource that is operated by the Illinois Campus Cluster Program (ICCP) in conjunction with the National Center for Supercomputing Applications (NCSA) and which is supported by funds from the University of Illinois at Urbana-Champaign.
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