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Cho Y, Nandy A, Duan C, Kulik HJ. DFT-Based Multireference Diagnostics in the Solid State: Application to Metal-Organic Frameworks. J Chem Theory Comput 2023; 19:190-197. [PMID: 36548116 DOI: 10.1021/acs.jctc.2c01033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
When a many-body wave function of a system cannot be captured by a single determinant, high-level multireference (MR) methods are required to properly explain its electronic structure. MR diagnostics to estimate the magnitude of such static correlation have been primarily developed for molecular systems and range from low in computational cost to as costly as the full MR calculation itself. We report the first application of low-cost MR diagnostics based on the fractional occupation number calculated with finite-temperature DFT to solid-state systems. To compare the behavior of the diagnostics on solids and molecules, we select metal-organic frameworks (MOFs) as model materials because their reticular nature provides an intuitive way to identify molecular derivatives. On a series of closed-shell MOFs, we demonstrate that the DFT-based MR diagnostics are equally applicable to solids as to their molecular derivatives. The magnitude and spatial distribution of the MR character of a MOF are found to have a good correlation with those of its molecular derivatives, which can be calculated much more affordably in comparison to those of the full MOF. The additivity of MR character discussed here suggests the set of molecular derivatives to be a good representation of a MOF for both MR detection and ultimately for MR corrections, facilitating accurate and efficient high-throughput screening of MOFs and other porous solids.
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Affiliation(s)
- Yeongsu Cho
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts02139, United States
| | - Aditya Nandy
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts02139, United States.,Department of Chemistry, Massachusetts Institute of Technology, Cambridge, Massachusetts02139, United States
| | - Chenru Duan
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts02139, United States.,Department of Chemistry, Massachusetts Institute of Technology, Cambridge, Massachusetts02139, United States
| | - Heather J Kulik
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts02139, United States.,Department of Chemistry, Massachusetts Institute of Technology, Cambridge, Massachusetts02139, United States
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Petras HR, Van Benschoten WZ, Ramadugu SK, Shepherd JJ. The Sign Problem in Density Matrix Quantum Monte Carlo. J Chem Theory Comput 2021; 17:6036-6052. [PMID: 34546738 PMCID: PMC8515812 DOI: 10.1021/acs.jctc.1c00078] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Abstract
Density matrix quantum Monte Carlo (DMQMC) is a recently developed method for stochastically sampling the N-particle thermal density matrix to obtain exact-on-average energies for model and ab initio systems. We report a systematic numerical study of the sign problem in DMQMC based on simulations of atomic and molecular systems. In DMQMC, the density matrix is written in an outer product basis of Slater determinants. In principle, this means that DMQMC needs to sample a space that scales in the system size, N, as O[(exp(N))2]. In practice, removing the sign problem requires a total walker population that exceeds a system-dependent critical walker population (Nc), imposing limitations on both storage and compute time. We establish that Nc for DMQMC is the square of Nc for FCIQMC. By contrast, the minimum Nc in the interaction picture modification of DMQMC (IP-DMQMC) is only linearly related to the Nc for FCIQMC. We find that this difference originates from the difference in propagation of IP-DMQMC versus canonical DMQMC: the former is asymmetric, whereas the latter is symmetric. When an asymmetric mode of propagation is used in DMQMC, there is a much greater stochastic error and is thus prohibitively expensive for DMQMC without the interaction picture adaptation. Finally, we find that the equivalence between IP-DMQMC and FCIQMC seems to extend to the initiator approximation, which is often required to study larger systems with large basis sets. This suggests that IP-DMQMC offers a way to ameliorate the cost of moving between a Slater determinant space and an outer product basis.
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Affiliation(s)
- Hayley R Petras
- Department of Chemistry, University of Iowa, Iowa City, Iowa 52242-1294, United States
| | | | - Sai Kumar Ramadugu
- Department of Chemistry, University of Iowa, Iowa City, Iowa 52242-1294, United States
| | - James J Shepherd
- Department of Chemistry, University of Iowa, Iowa City, Iowa 52242-1294, United States
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Sun Q, Zhang X, Banerjee S, Bao P, Barbry M, Blunt NS, Bogdanov NA, Booth GH, Chen J, Cui ZH, Eriksen JJ, Gao Y, Guo S, Hermann J, Hermes MR, Koh K, Koval P, Lehtola S, Li Z, Liu J, Mardirossian N, McClain JD, Motta M, Mussard B, Pham HQ, Pulkin A, Purwanto W, Robinson PJ, Ronca E, Sayfutyarova ER, Scheurer M, Schurkus HF, Smith JET, Sun C, Sun SN, Upadhyay S, Wagner LK, Wang X, White A, Whitfield JD, Williamson MJ, Wouters S, Yang J, Yu JM, Zhu T, Berkelbach TC, Sharma S, Sokolov AY, Chan GKL. Recent developments in the PySCF program package. J Chem Phys 2020; 153:024109. [DOI: 10.1063/5.0006074] [Citation(s) in RCA: 151] [Impact Index Per Article: 37.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023] Open
Affiliation(s)
- Qiming Sun
- AxiomQuant Investment Management LLC, Shanghai 200120, China
| | - Xing Zhang
- Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, California 91125, USA
| | - Samragni Banerjee
- Department of Chemistry and Biochemistry, The Ohio State University, Columbus, Ohio 43210, USA
| | - Peng Bao
- Beijing National Laboratory for Molecular Sciences, State Key Laboratory for Structural Chemistry of Unstable and Stable Species, Institute of Chemistry, Chinese Academy of Sciences, Beijing 100190, China
| | - Marc Barbry
- Simbeyond B.V., P.O. Box 513, NL-5600 MB, Eindhoven, The Netherlands
| | - Nick S. Blunt
- Department of Chemistry, Lensfield Road, Cambridge CB2 1EW, United Kingdom
| | - Nikolay A. Bogdanov
- Max Planck Institute for Solid State Research, Heisenbergstraße 1, 70569 Stuttgart, Germany
| | - George H. Booth
- Department of Physics, King’s College London, Strand, London WC2R 2LS, United Kingdom
| | - Jia Chen
- Department of Physics, University of Florida, Gainesville, Florida 32611, USA
- Quantum Theory Project, University of Florida, Gainesville, Florida 32611, USA
| | - Zhi-Hao Cui
- Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, California 91125, USA
| | - Janus J. Eriksen
- School of Chemistry, University of Bristol, Cantock’s Close, Bristol BS8 1TS, United Kingdom
| | - Yang Gao
- Division of Engineering and Applied Science, California Institute of Technology, Pasadena, California 91125, USA
| | - Sheng Guo
- Google Inc., Mountain View, California 94043, USA
| | - Jan Hermann
- FU Berlin, Department of Mathematics and Computer Science, Arnimallee 6, 14195 Berlin, Germany
- TU Berlin, Machine Learning Group, Marchstr. 23, 10587 Berlin, Germany
| | - Matthew R. Hermes
- Department of Chemistry, Chemical Theory Center, and Supercomputing Institute, University of Minnesota, 207 Pleasant Street SE, Minneapolis, Minnesota 55455, USA
| | - Kevin Koh
- Department of Chemistry and Biochemistry, The University of Notre Dame du Lac, 251 Nieuwland Science Hall, Notre Dame, Indiana 46556, USA
| | - Peter Koval
- Simune Atomistics S.L., Avenida Tolosa 76, Donostia-San Sebastian, Spain
| | - Susi Lehtola
- Department of Chemistry, University of Helsinki, P.O. Box 55 (A. I. Virtasen aukio 1), FI-00014 Helsinki, Finland
| | - Zhendong Li
- Key Laboratory of Theoretical and Computational Photochemistry, Ministry of Education, College of Chemistry, Beijing Normal University, Beijing 100875, China
| | - Junzi Liu
- Department of Chemistry, The Johns Hopkins University, Baltimore, Maryland 21218, USA
| | - Narbe Mardirossian
- AMGEN Research, One Amgen Center Drive, Thousand Oaks, California 91320, USA
| | | | - Mario Motta
- IBM Almaden Research Center, San Jose, California 95120, USA
| | - Bastien Mussard
- Department of Chemistry, University of Colorado, Boulder, Colorado 80302, USA
| | - Hung Q. Pham
- Department of Chemistry, Chemical Theory Center, and Supercomputing Institute, University of Minnesota, 207 Pleasant Street SE, Minneapolis, Minnesota 55455, USA
| | - Artem Pulkin
- QuTech and Kavli Institute of Nanoscience, Delft University of Technology, The Netherlands
| | - Wirawan Purwanto
- Information Technology Services, Old Dominion University, Norfolk, Virginia 23529, USA
| | - Paul J. Robinson
- Department of Chemistry, Columbia University, New York, New York 10027, USA
| | - Enrico Ronca
- Istituto per i Processi Chimico Fisici del CNR (IPCF-CNR), Via G. Moruzzi, 1, 56124 Pisa, Italy
| | - Elvira R. Sayfutyarova
- Department of Chemistry, Yale University, 225 Prospect Street, New Haven, Connecticut 06520, USA
| | - Maximilian Scheurer
- Interdisciplinary Center for Scientific Computing, Ruprecht-Karls University of Heidelberg, 205 Im Neuenheimer Feld, 69120 Heidelberg, Germany
| | - Henry F. Schurkus
- Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, California 91125, USA
| | - James E. T. Smith
- Department of Chemistry, University of Colorado, Boulder, Colorado 80302, USA
| | - Chong Sun
- Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, California 91125, USA
| | - Shi-Ning Sun
- Division of Engineering and Applied Science, California Institute of Technology, Pasadena, California 91125, USA
| | - Shiv Upadhyay
- Department of Chemistry, University of Pittsburgh, Pittsburgh, Pennsylvania 15260, USA
| | - Lucas K. Wagner
- Department of Physics and Institute for Condensed Matter Theory, University of Illinois at Urbana-Champaign, Illinois 61801, USA
| | - Xiao Wang
- Center for Computational Quantum Physics, Flatiron Institute, New York, New York 10010, USA
| | - Alec White
- Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, California 91125, USA
| | - James Daniel Whitfield
- Department of Physics and Astronomy, Dartmouth College, Hanover, New Hampshire 03755, USA
| | - Mark J. Williamson
- Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, United Kingdom
| | | | - Jun Yang
- Department of Chemistry, The University of Hong Kong, Pokfulam Road, Hong Kong SAR, China
| | - Jason M. Yu
- Department of Chemistry, University of California, Irvine, 1102 Natural Sciences II, Irvine, California 92697-2025, USA
| | - Tianyu Zhu
- Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, California 91125, USA
| | - Timothy C. Berkelbach
- Department of Chemistry, Columbia University, New York, New York 10027, USA
- Center for Computational Quantum Physics, Flatiron Institute, New York, New York 10010, USA
| | - Sandeep Sharma
- Department of Chemistry, University of Colorado, Boulder, Colorado 80302, USA
| | - Alexander Yu. Sokolov
- Department of Chemistry and Biochemistry, The Ohio State University, Columbus, Ohio 43210, USA
| | - Garnet Kin-Lic Chan
- Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, California 91125, USA
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