Bypassing the computational bottleneck of quantum-embedding theories for strong electron correlations with machine learning
A cardinal obstacle to performing quantum-mechanical simulations of strongly correlated matter is that, with the theoretical tools presently available, sufficiently accurate computations are often too expensive to be ever feasible. Here we design a computational framework combining quantum-embedding...
Main Authors: | John Rogers, Tsung-Han Lee, Sahar Pakdel, Wenhu Xu, Vladimir Dobrosavljević, Yong-Xin Yao, Ove Christiansen, Nicola Lanatà |
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Format: | Article |
Language: | English |
Published: |
American Physical Society
2021-01-01
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Series: | Physical Review Research |
Online Access: | http://doi.org/10.1103/PhysRevResearch.3.013101 |
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