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: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
American Physical Society
2021-01-01
|
| Series: | Physical Review Research |
| Online Access: | http://doi.org/10.1103/PhysRevResearch.3.013101 |
| _version_ | 1827285849531744256 |
|---|---|
| author | John Rogers Tsung-Han Lee Sahar Pakdel Wenhu Xu Vladimir Dobrosavljević Yong-Xin Yao Ove Christiansen Nicola Lanatà |
| author_facet | John Rogers Tsung-Han Lee Sahar Pakdel Wenhu Xu Vladimir Dobrosavljević Yong-Xin Yao Ove Christiansen Nicola Lanatà |
| author_sort | John Rogers |
| collection | DOAJ |
| description | 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 (QE) methods with machine learning. This allows us to bypass altogether the most computationally expensive components of QE algorithms, making their overall cost comparable to bare density functional theory. We perform benchmark calculations of a series of actinide systems, where our method accurately describes the correlation effects, reducing by orders of magnitude the computational cost. We argue that, by producing a larger-scale set of training data, it will be possible to apply our method to systems with arbitrary stoichiometries and crystal structures, paving the way to virtually infinite applications in condensed matter physics, chemistry, and materials science. |
| first_indexed | 2024-04-24T10:21:37Z |
| format | Article |
| id | doaj.art-64dd889c7bbc443fb56ccbc69826c7d3 |
| institution | Directory Open Access Journal |
| issn | 2643-1564 |
| language | English |
| last_indexed | 2024-04-24T10:21:37Z |
| publishDate | 2021-01-01 |
| publisher | American Physical Society |
| record_format | Article |
| series | Physical Review Research |
| spelling | doaj.art-64dd889c7bbc443fb56ccbc69826c7d32024-04-12T17:06:49ZengAmerican Physical SocietyPhysical Review Research2643-15642021-01-013101310110.1103/PhysRevResearch.3.013101Bypassing the computational bottleneck of quantum-embedding theories for strong electron correlations with machine learningJohn RogersTsung-Han LeeSahar PakdelWenhu XuVladimir DobrosavljevićYong-Xin YaoOve ChristiansenNicola LanatàA 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 (QE) methods with machine learning. This allows us to bypass altogether the most computationally expensive components of QE algorithms, making their overall cost comparable to bare density functional theory. We perform benchmark calculations of a series of actinide systems, where our method accurately describes the correlation effects, reducing by orders of magnitude the computational cost. We argue that, by producing a larger-scale set of training data, it will be possible to apply our method to systems with arbitrary stoichiometries and crystal structures, paving the way to virtually infinite applications in condensed matter physics, chemistry, and materials science.http://doi.org/10.1103/PhysRevResearch.3.013101 |
| spellingShingle | John Rogers Tsung-Han Lee Sahar Pakdel Wenhu Xu Vladimir Dobrosavljević Yong-Xin Yao Ove Christiansen Nicola Lanatà Bypassing the computational bottleneck of quantum-embedding theories for strong electron correlations with machine learning Physical Review Research |
| title | Bypassing the computational bottleneck of quantum-embedding theories for strong electron correlations with machine learning |
| title_full | Bypassing the computational bottleneck of quantum-embedding theories for strong electron correlations with machine learning |
| title_fullStr | Bypassing the computational bottleneck of quantum-embedding theories for strong electron correlations with machine learning |
| title_full_unstemmed | Bypassing the computational bottleneck of quantum-embedding theories for strong electron correlations with machine learning |
| title_short | Bypassing the computational bottleneck of quantum-embedding theories for strong electron correlations with machine learning |
| title_sort | bypassing the computational bottleneck of quantum embedding theories for strong electron correlations with machine learning |
| url | http://doi.org/10.1103/PhysRevResearch.3.013101 |
| work_keys_str_mv | AT johnrogers bypassingthecomputationalbottleneckofquantumembeddingtheoriesforstrongelectroncorrelationswithmachinelearning AT tsunghanlee bypassingthecomputationalbottleneckofquantumembeddingtheoriesforstrongelectroncorrelationswithmachinelearning AT saharpakdel bypassingthecomputationalbottleneckofquantumembeddingtheoriesforstrongelectroncorrelationswithmachinelearning AT wenhuxu bypassingthecomputationalbottleneckofquantumembeddingtheoriesforstrongelectroncorrelationswithmachinelearning AT vladimirdobrosavljevic bypassingthecomputationalbottleneckofquantumembeddingtheoriesforstrongelectroncorrelationswithmachinelearning AT yongxinyao bypassingthecomputationalbottleneckofquantumembeddingtheoriesforstrongelectroncorrelationswithmachinelearning AT ovechristiansen bypassingthecomputationalbottleneckofquantumembeddingtheoriesforstrongelectroncorrelationswithmachinelearning AT nicolalanata bypassingthecomputationalbottleneckofquantumembeddingtheoriesforstrongelectroncorrelationswithmachinelearning |