1
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Chanda S, Sen S. Benchmark computations of nearly degenerate singlet and triplet states of N-heterocyclic chromophores. I. Wavefunction-based methods. J Chem Phys 2024; 161:174117. [PMID: 39503472 DOI: 10.1063/5.0225537] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2024] [Accepted: 08/07/2024] [Indexed: 11/08/2024] Open
Abstract
In this paper, we investigate the role of electron correlation in predicting the S1-S0 and T1-S0 excitation energies and, hence, the singlet-triplet gap (ΔEST) in a set of cyclazines, which act as templates for potential candidates for fifth generation organic light emitting diode materials. This issue has recently garnered much interest with the focus being on the inversion of the ΔEST, although experiments have indicated near degenerate levels with both positive and negative being within the experimental error bar [J. Am. Chem. Soc. 102, 6068 (1980), J. Am. Chem. Soc. 108, 17(1986)]. We have carried out a systematic and exhaustive study of various excited state electronic structure methodologies and identified the strengths and shortcomings of the various approaches and approximations in view of this challenging case. We have found that near degeneracy can be achieved either with a proper balance of static and dynamic correlation in multireference theories or with state-specific orbital corrections, including its coupling with correlation. The role of spin contamination is also discussed. Eventually, this paper seeks to produce benchmark numbers for establishing cost-effective theories, which can then be used for screening derivatives of these templates with desirable optical and structural properties. Additionally, we would like to point out that the use of domain-based local pair natural orbital-similarity transformed EOM-coupled cluster singles and doubles as the benchmark for ΔEST [as used in J. Phys. Chem. A 126(8), 1378 (2022), Chem. Phys. Lett. 779, 138827 (2021)] is not a suitable benchmark for these classes of molecules.
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Affiliation(s)
- Shamik Chanda
- Department of Chemical Sciences, Indian Institute of Science Education and Research (IISER) Kolkata Nadia, Mohanpur 741246, West Bengal, India
| | - Sangita Sen
- Department of Chemical Sciences, Indian Institute of Science Education and Research (IISER) Kolkata Nadia, Mohanpur 741246, West Bengal, India
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2
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Greiner J, Gauss J, Eriksen JJ. Error Control and Automatic Detection of Reference Active Spaces in Many-Body Expanded Full Configuration Interaction. J Phys Chem A 2024; 128:6806-6818. [PMID: 39099303 DOI: 10.1021/acs.jpca.4c04056] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/06/2024]
Abstract
We present a wide-reaching revamp of the generalized many-body expanded full configuration interaction (MBE-FCI) method. First, we outline how to automatize the selection of reference active spaces, whereby the inherent bias introduced through a manual identification is reduced, also within the context of traditional complete active space methods. Second, we allow for the use of compact orbital clusters as expansion objects, which works to circumvent the unfavorable scaling with the number of orbitals included in the space complementary to the reference orbitals. Finally, we present a new algorithm for ensuring that many-body expansions can be efficiently terminated while conservatively accounting for resulting errors. These developments are all tested on a variety of molecular systems and different orbital representations to illustrate the abilities of our algorithm to produce correlation energies within predetermined error bounds, significantly broadening the overall applicability of the MBE-FCI method.
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Affiliation(s)
- Jonas Greiner
- Department Chemie, Johannes Gutenberg-Universität Mainz Duesbergweg 10-14, 55128 Mainz, Germany
| | - Jürgen Gauss
- Department Chemie, Johannes Gutenberg-Universität Mainz Duesbergweg 10-14, 55128 Mainz, Germany
| | - Janus J Eriksen
- DTU Chemistry, Technical University of Denmark, Kemitorvet Bldg. 206, 2800 Kgs. Lyngby, Denmark
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3
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Wang Q, Agarawal V, Hermes MR, Motta M, Rice JE, Jones GO, Gagliardi L. Distinguishing homolytic vs heterolytic bond dissociation of phenylsulfonium cations with localized active space methods. J Chem Phys 2024; 161:014106. [PMID: 38949581 DOI: 10.1063/5.0215697] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2024] [Accepted: 06/12/2024] [Indexed: 07/02/2024] Open
Abstract
Modeling chemical reactions with quantum chemical methods is challenging when the electronic structure varies significantly throughout the reaction and when electronic excited states are involved. Multireference methods, such as complete active space self-consistent field (CASSCF), can handle these multiconfigurational situations. However, even if the size of the needed active space is affordable, in many cases, the active space does not change consistently from reactant to product, causing discontinuities in the potential energy surface. The localized active space SCF (LASSCF) is a cheaper alternative to CASSCF for strongly correlated systems with weakly correlated fragments. The method is used for the first time to study a chemical reaction, namely the bond dissociation of a mono-, di-, and triphenylsulfonium cation. LASSCF calculations generate smooth potential energy scans more easily than the corresponding, more computationally expensive CASSCF calculations while predicting similar bond dissociation energies. Our calculations suggest a homolytic bond cleavage for di- and triphenylsulfonium and a heterolytic pathway for monophenylsulfonium.
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Affiliation(s)
- Qiaohong Wang
- Pritzker School of Molecular Engineering, University of Chicago, Chicago, Illinois 60637, USA
| | - Valay Agarawal
- Department of Chemistry, Chicago Center for Theoretical Chemistry, University of Chicago, Chicago, Illinois 60637, USA
| | - Matthew R Hermes
- Department of Chemistry, Chicago Center for Theoretical Chemistry, University of Chicago, Chicago, Illinois 60637, USA
| | - Mario Motta
- IBM Quantum, IBM T. J. Watson Research Center, Yorktown Heights, New York 1059, USA
| | - Julia E Rice
- IBM Quantum, IBM Research-Almaden, San Jose, California 95120, USA
| | - Gavin O Jones
- IBM Quantum, IBM Research-Almaden, San Jose, California 95120, USA
| | - Laura Gagliardi
- Pritzker School of Molecular Engineering, University of Chicago, Chicago, Illinois 60637, USA
- Department of Chemistry, Chicago Center for Theoretical Chemistry, University of Chicago, Chicago, Illinois 60637, USA
- James Franck Institute, University of Chicago, Chicago, Illinois 60637, USA
- Argonne National Laboratory, 9700 S, Cass Avenue, Lemont, Illinois 60439, USA
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4
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Aldossary A, Campos-Gonzalez-Angulo JA, Pablo-García S, Leong SX, Rajaonson EM, Thiede L, Tom G, Wang A, Avagliano D, Aspuru-Guzik A. In Silico Chemical Experiments in the Age of AI: From Quantum Chemistry to Machine Learning and Back. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024; 36:e2402369. [PMID: 38794859 DOI: 10.1002/adma.202402369] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/15/2024] [Revised: 04/28/2024] [Indexed: 05/26/2024]
Abstract
Computational chemistry is an indispensable tool for understanding molecules and predicting chemical properties. However, traditional computational methods face significant challenges due to the difficulty of solving the Schrödinger equations and the increasing computational cost with the size of the molecular system. In response, there has been a surge of interest in leveraging artificial intelligence (AI) and machine learning (ML) techniques to in silico experiments. Integrating AI and ML into computational chemistry increases the scalability and speed of the exploration of chemical space. However, challenges remain, particularly regarding the reproducibility and transferability of ML models. This review highlights the evolution of ML in learning from, complementing, or replacing traditional computational chemistry for energy and property predictions. Starting from models trained entirely on numerical data, a journey set forth toward the ideal model incorporating or learning the physical laws of quantum mechanics. This paper also reviews existing computational methods and ML models and their intertwining, outlines a roadmap for future research, and identifies areas for improvement and innovation. Ultimately, the goal is to develop AI architectures capable of predicting accurate and transferable solutions to the Schrödinger equation, thereby revolutionizing in silico experiments within chemistry and materials science.
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Affiliation(s)
- Abdulrahman Aldossary
- Department of Chemistry, University of Toronto, 80 St. George Street, Toronto, ON, M5S 3H6, Canada
| | | | - Sergio Pablo-García
- Department of Chemistry, University of Toronto, 80 St. George Street, Toronto, ON, M5S 3H6, Canada
- Department of Computer Science, University of Toronto, 40 St. George Street, Toronto, ON, M5S 2E4, Canada
| | - Shi Xuan Leong
- Department of Chemistry, University of Toronto, 80 St. George Street, Toronto, ON, M5S 3H6, Canada
| | - Ella Miray Rajaonson
- Department of Chemistry, University of Toronto, 80 St. George Street, Toronto, ON, M5S 3H6, Canada
- Vector Institute for Artificial Intelligence, 661 University Ave. Suite 710, Toronto, ON, M5G 1M1, Canada
| | - Luca Thiede
- Department of Computer Science, University of Toronto, 40 St. George Street, Toronto, ON, M5S 2E4, Canada
- Vector Institute for Artificial Intelligence, 661 University Ave. Suite 710, Toronto, ON, M5G 1M1, Canada
| | - Gary Tom
- Department of Chemistry, University of Toronto, 80 St. George Street, Toronto, ON, M5S 3H6, Canada
- Vector Institute for Artificial Intelligence, 661 University Ave. Suite 710, Toronto, ON, M5G 1M1, Canada
| | - Andrew Wang
- Department of Chemistry, University of Toronto, 80 St. George Street, Toronto, ON, M5S 3H6, Canada
| | - Davide Avagliano
- Chimie ParisTech, PSL University, CNRS, Institute of Chemistry for Life and Health Sciences (iCLeHS UMR 8060), Paris, F-75005, France
| | - Alán Aspuru-Guzik
- Department of Chemistry, University of Toronto, 80 St. George Street, Toronto, ON, M5S 3H6, Canada
- Department of Computer Science, University of Toronto, 40 St. George Street, Toronto, ON, M5S 2E4, Canada
- Vector Institute for Artificial Intelligence, 661 University Ave. Suite 710, Toronto, ON, M5G 1M1, Canada
- Department of Materials Science & Engineering, University of Toronto, 184 College St., Toronto, ON, M5S 3E4, Canada
- Department of Chemical Engineering & Applied Chemistry, University of Toronto, 200 College St., Toronto, ON, M5S 3E5, Canada
- Lebovic Fellow, Canadian Institute for Advanced Research (CIFAR), 66118 University Ave., Toronto, M5G 1M1, Canada
- Acceleration Consortium, 80 St George St, Toronto, M5S 3H6, Canada
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5
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Vaquero-Sabater N, Carreras A, Orús R, Mayhall NJ, Casanova D. Physically Motivated Improvements of Variational Quantum Eigensolvers. J Chem Theory Comput 2024; 20:5133-5144. [PMID: 38853416 PMCID: PMC11209943 DOI: 10.1021/acs.jctc.4c00329] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2024] [Revised: 05/29/2024] [Accepted: 05/30/2024] [Indexed: 06/11/2024]
Abstract
The adaptive derivative-assembled pseudo-Trotter variational quantum eigensolver (ADAPT-VQE) has emerged as a pivotal promising approach for electronic structure challenges in quantum chemistry with noisy quantum devices. Nevertheless, to surmount existing technological constraints, this study endeavors to enhance ADAPT-VQE's efficacy. Leveraging insights from the electronic structure theory, we concentrate on optimizing state preparation without added computational burden and guiding ansatz expansion to yield more concise wave functions with expedited convergence toward exact solutions. These advancements culminate in shallower circuits and, as demonstrated, reduced measurement requirements. This research delineates these enhancements and assesses their performance across mono, di, and tridimensional arrangements of H4 models, as well as in the water molecule. Ultimately, this work attests to the viability of physically motivated strategies in fortifying ADAPT-VQE's efficiency, marking a significant stride in quantum chemistry simulations.
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Affiliation(s)
- Nonia Vaquero-Sabater
- Donostia
International Physics Center(DIPC), Donostia 20018, Euskadi, Spain
- Polimero
eta Material Aurreratuak: Fisika, Kimika eta Teknologia Saila, Kimika
Fakultatea, Euskal Herriko Unibertsitatea (UPV/EHU), PK 1072, Donostia 20080, Euskadi, Spain
| | - Abel Carreras
- Multiverse
Computing, Donostia 20014, Euskadi, Spain
| | - Román Orús
- Donostia
International Physics Center(DIPC), Donostia 20018, Euskadi, Spain
- Multiverse
Computing, Donostia 20014, Euskadi, Spain
- IKERBASQUE,
Basque Foundation for Science, Bilbao 48009, Euskadi, Spain
| | - Nicholas J. Mayhall
- Department
of Chemistry, Virginia Tech, Blacksburg, Virginia 24061, United States
| | - David Casanova
- Donostia
International Physics Center(DIPC), Donostia 20018, Euskadi, Spain
- IKERBASQUE,
Basque Foundation for Science, Bilbao 48009, Euskadi, Spain
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6
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Weymuth T, Unsleber JP, Türtscher PL, Steiner M, Sobez JG, Müller CH, Mörchen M, Klasovita V, Grimmel SA, Eckhoff M, Csizi KS, Bosia F, Bensberg M, Reiher M. SCINE-Software for chemical interaction networks. J Chem Phys 2024; 160:222501. [PMID: 38857173 DOI: 10.1063/5.0206974] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2024] [Accepted: 05/09/2024] [Indexed: 06/12/2024] Open
Abstract
The software for chemical interaction networks (SCINE) project aims at pushing the frontier of quantum chemical calculations on molecular structures to a new level. While calculations on individual structures as well as on simple relations between them have become routine in chemistry, new developments have pushed the frontier in the field to high-throughput calculations. Chemical relations may be created by a search for specific molecular properties in a molecular design attempt, or they can be defined by a set of elementary reaction steps that form a chemical reaction network. The software modules of SCINE have been designed to facilitate such studies. The features of the modules are (i) general applicability of the applied methodologies ranging from electronic structure (no restriction to specific elements of the periodic table) to microkinetic modeling (with little restrictions on molecularity), full modularity so that SCINE modules can also be applied as stand-alone programs or be exchanged for external software packages that fulfill a similar purpose (to increase options for computational campaigns and to provide alternatives in case of tasks that are hard or impossible to accomplish with certain programs), (ii) high stability and autonomous operations so that control and steering by an operator are as easy as possible, and (iii) easy embedding into complex heterogeneous environments for molecular structures taken individually or in the context of a reaction network. A graphical user interface unites all modules and ensures interoperability. All components of the software have been made available as open source and free of charge.
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Affiliation(s)
- Thomas Weymuth
- ETH Zurich, Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 2, 8093 Zurich, Switzerland
| | - Jan P Unsleber
- ETH Zurich, Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 2, 8093 Zurich, Switzerland
| | - Paul L Türtscher
- ETH Zurich, Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 2, 8093 Zurich, Switzerland
| | - Miguel Steiner
- ETH Zurich, Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 2, 8093 Zurich, Switzerland
| | - Jan-Grimo Sobez
- ETH Zurich, Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 2, 8093 Zurich, Switzerland
| | - Charlotte H Müller
- ETH Zurich, Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 2, 8093 Zurich, Switzerland
| | - Maximilian Mörchen
- ETH Zurich, Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 2, 8093 Zurich, Switzerland
| | - Veronika Klasovita
- ETH Zurich, Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 2, 8093 Zurich, Switzerland
| | - Stephanie A Grimmel
- ETH Zurich, Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 2, 8093 Zurich, Switzerland
| | - Marco Eckhoff
- ETH Zurich, Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 2, 8093 Zurich, Switzerland
| | - Katja-Sophia Csizi
- ETH Zurich, Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 2, 8093 Zurich, Switzerland
| | - Francesco Bosia
- ETH Zurich, Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 2, 8093 Zurich, Switzerland
| | - Moritz Bensberg
- ETH Zurich, Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 2, 8093 Zurich, Switzerland
| | - Markus Reiher
- ETH Zurich, Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 2, 8093 Zurich, Switzerland
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7
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Hehn L, Deglmann P, Kühn M. Chelate Complexes of 3d Transition Metal Ions─A Challenge for Electronic-Structure Methods? J Chem Theory Comput 2024; 20:4545-4568. [PMID: 38805381 DOI: 10.1021/acs.jctc.3c01375] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/30/2024]
Abstract
Different electronic-structure methods were assessed for their ability to predict two important properties of the industrially relevant chelating agent nitrilotriacetic acid (NTA): its selectivity with respect to six different first-row transition metal ions and the spin-state energetics of its complex with Fe(III). The investigated methods encompassed density functional theory (DFT), the random phase approximation (RPA), coupled cluster (CC) theory, and the auxiliary-field quantum Monte Carlo (AFQMC) method, as well as the complete active space self-consistent field (CASSCF) method and the respective on-top methods: second-order N-electron valence state perturbation theory (NEVPT2) and multiconfiguration pair-density functional theory (MC-PDFT). Different strategies for selecting active spaces were explored, and the density matrix renormalization group (DMRG) approach was used to solve the largest active spaces. Despite somewhat ambiguous multi-reference diagnostics, most methods gave relatively good agreement with experimental data for the chemical reactions connected to the selectivity, which only involved transition-metal complexes in their high-spin state. CC methods yielded the highest accuracy followed by range-separated DFT and AFQMC. We discussed in detail that even higher accuracies can be obtained with NEVPT2, under the prerequisite that consistent active spaces along the entire chemical reaction can be selected, which was not the case for reactions involving Fe(III). A bigger challenge for electronic-structure methods was the prediction of the spin-state energetics, which additionally involved lower spin states that exhibited larger multi-reference diagnostics. Conceptually different, typically accurate methods ranging from CC theory via DMRG-NEVPT2 in combination with large active spaces to AFQMC agreed well that the high-spin state is energetically significantly favored over the other spin states. This was in contrast to most DFT functionals and RPA which yielded a smaller stabilization and some common DFT functionals and MC-PDFT even predicting the low-spin state to be energetically most favorable.
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Affiliation(s)
- Lukas Hehn
- Next Generation Computing, BASF SE, Pfalzgrafenstr. 1, 67061 Ludwigshafen, Germany
| | - Peter Deglmann
- Quantum Chemistry, BASF SE, Carl-Bosch-Str. 38, 67063 Ludwigshafen, Germany
| | - Michael Kühn
- Next Generation Computing, BASF SE, Pfalzgrafenstr. 1, 67061 Ludwigshafen, Germany
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8
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Wardzala J, King DS, Ogunfowora L, Savoie B, Gagliardi L. Organic Reactivity Made Easy and Accurate with Automated Multireference Calculations. ACS CENTRAL SCIENCE 2024; 10:833-841. [PMID: 38680571 PMCID: PMC11046455 DOI: 10.1021/acscentsci.3c01559] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/14/2023] [Revised: 02/29/2024] [Accepted: 03/01/2024] [Indexed: 05/01/2024]
Abstract
In organic reactivity studies, quantum chemical calculations play a pivotal role as the foundation of understanding and machine learning model development. While prevalent black-box methods like density functional theory (DFT) and coupled-cluster theory (e.g., CCSD(T)) have significantly advanced our understanding of chemical reactivity, they frequently fall short in describing multiconfigurational transition states and intermediates. Achieving a more accurate description necessitates the use of multireference methods. However, these methods have not been used at scale due to their often-faulty predictions without expert input. Here, we overcome this deficiency with automated multiconfigurational pair-density functional theory (MC-PDFT) calculations. We apply this method to 908 automatically generated organic reactions. We find 68% of these reactions present significant multiconfigurational character in which the automated multiconfigurational approach often provides a more accurate and/or efficient description than DFT and CCSD(T). This work presents the first high-throughput application of automated multiconfigurational methods to reactivity, enabled by automated active space selection algorithms and the computation of electronic correlation with MC-PDFT on-top functionals. This approach can be used in a black-box fashion, avoiding significant active space inconsistency error in both single- and multireference cases and providing accurate multiconfigurational descriptions when needed.
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Affiliation(s)
- Jacob
J. Wardzala
- Department
of Chemistry,University of Chicago, Chicago, Illinois 60637, United States
| | - Daniel S. King
- Department
of Chemistry,University of Chicago, Chicago, Illinois 60637, United States
| | - Lawal Ogunfowora
- Davidson
School of Chemical Engineering, Purdue University, West Lafayette, Indiana 47906, United States
| | - Brett Savoie
- Davidson
School of Chemical Engineering, Purdue University, West Lafayette, Indiana 47906, United States
| | - Laura Gagliardi
- Department
of Chemistry, Pritzker School of Molecular Engineering, James Franck
Institute, Chicago Center for Theoretical Chemistry, University of Chicago, Chicago, Illinois 60637, United States
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9
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King DS, Truhlar DG, Gagliardi L. Variational Active Space Selection with Multiconfiguration Pair-Density Functional Theory. J Chem Theory Comput 2023; 19:8118-8128. [PMID: 37905518 DOI: 10.1021/acs.jctc.3c00792] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2023]
Abstract
The selection of an adequate set of active orbitals for modeling strongly correlated electronic states is difficult to automate because it is highly dependent on the states and molecule of interest. Although many approaches have shown some success, no single approach has worked well in all cases. In light of this, we present the "discrete variational selection" (DVS) approach to active space selection, in which one generates multiple trial wave functions from a diverse set of systematically constructed active spaces and then selects between these wave functions variationally. We apply this DVS approach to 207 vertical excitations of small-to-medium-sized organic and inorganic molecules (with 3 to 18 atoms) in the QUESTDB database by (i) constructing various sets of active space orbitals through diagonalization of parametrized operators and (ii) choosing the result with the lowest average energy among the states of interest. This approach proves ineffective when variationally selecting between wave functions using the density matrix renormalization group (DMRG) or complete active space self-consistent field (CASSCF) energy but is able to provide good results when variationally selecting between wave functions using the energy of the translated PBE (tPBE) functional from multiconfiguration pair-density functional theory (MC-PDFT). Applying this DVS-tPBE approach to selection among state-averaged DMRG wave functions, we obtain a mean unsigned error of only 0.17 eV using hybrid MC-PDFT. This result matches that of our previous benchmark without the need to filter out poor active spaces and with no further orbital optimization following active space selection of the SA-DMRG wave functions. Furthermore, we find that DVS-tPBE is able to robustly and effectively select between the new SA-DMRG wave functions and our previous SA-CASSCF results.
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Affiliation(s)
- Daniel S King
- Department of Chemistry, University of Chicago, Chicago, Illinois 60637, United States
| | - Donald G Truhlar
- Department of Chemistry, Chemical Theory Group, and Supercomputing Institute, University of Minnesota, Minneapolis, Minnesota 55455, United States
| | - Laura Gagliardi
- Department of Chemistry, Pritzker School of Molecular Engineering, James Franck Institute, Chicago Center for Theoretical Chemistry, University of Chicago, Chicago, Illinois 60637, United States
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10
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Hennefarth MR, King DS, Gagliardi L. Linearized Pair-Density Functional Theory for Vertical Excitation Energies. J Chem Theory Comput 2023; 19:7983-7988. [PMID: 37877741 DOI: 10.1021/acs.jctc.3c00863] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2023]
Abstract
Multiconfiguration pair-density functional theory (MC-PDFT) is a computationally efficient method that computes the energies of electronic states in a state specific or state average framework via an on-top functional. However, MC-PDFT does not include state interaction among these states since the final energies do not come from the diagonalization of an effective model-space Hamiltonian. Recently, multistate extensions such as linearized PDFT (L-PDFT) have been developed to accurately model the potentials near conical intersections and avoided crossings. However, there has not been any systematic study evaluating their performance for predicting vertical excitations at the equilibrium geometry of a molecule, when the excited states are generally well separated. In this paper, we report the performance of L-PDFT on the extensive QUESTDB data set of vertical excitations using a database of automatically selected active spaces. We show that L-PDFT performs well on all these excitations and successfully reproduces the performance of MC-PDFT. These results further demonstrate the potential of L-PDFT, as its scaling is constant with the number of states included in the state-average manifold, whereas MC-PDFT scales linearly in this regard.
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Affiliation(s)
| | | | - Laura Gagliardi
- Argonne National Laboratory, 9700 S. Cass Avenue, Lemont, Illinois 60439, United States
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11
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Li Manni G, Fdez. Galván I, Alavi A, Aleotti F, Aquilante F, Autschbach J, Avagliano D, Baiardi A, Bao JJ, Battaglia S, Birnoschi L, Blanco-González A, Bokarev SI, Broer R, Cacciari R, Calio PB, Carlson RK, Carvalho Couto R, Cerdán L, Chibotaru LF, Chilton NF, Church JR, Conti I, Coriani S, Cuéllar-Zuquin J, Daoud RE, Dattani N, Decleva P, de Graaf C, Delcey M, De Vico L, Dobrautz W, Dong SS, Feng R, Ferré N, Filatov(Gulak) M, Gagliardi L, Garavelli M, González L, Guan Y, Guo M, Hennefarth MR, Hermes MR, Hoyer CE, Huix-Rotllant M, Jaiswal VK, Kaiser A, Kaliakin DS, Khamesian M, King DS, Kochetov V, Krośnicki M, Kumaar AA, Larsson ED, Lehtola S, Lepetit MB, Lischka H, López Ríos P, Lundberg M, Ma D, Mai S, Marquetand P, Merritt ICD, Montorsi F, Mörchen M, Nenov A, Nguyen VHA, Nishimoto Y, Oakley MS, Olivucci M, Oppel M, Padula D, Pandharkar R, Phung QM, Plasser F, Raggi G, Rebolini E, Reiher M, Rivalta I, Roca-Sanjuán D, Romig T, Safari AA, Sánchez-Mansilla A, Sand AM, Schapiro I, Scott TR, Segarra-Martí J, Segatta F, Sergentu DC, Sharma P, Shepard R, Shu Y, Staab JK, Straatsma TP, Sørensen LK, Tenorio BNC, Truhlar DG, Ungur L, Vacher M, Veryazov V, Voß TA, Weser O, Wu D, Yang X, Yarkony D, Zhou C, Zobel JP, Lindh R. The OpenMolcas Web: A Community-Driven Approach to Advancing Computational Chemistry. J Chem Theory Comput 2023; 19:6933-6991. [PMID: 37216210 PMCID: PMC10601490 DOI: 10.1021/acs.jctc.3c00182] [Citation(s) in RCA: 91] [Impact Index Per Article: 45.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Indexed: 05/24/2023]
Abstract
The developments of the open-source OpenMolcas chemistry software environment since spring 2020 are described, with a focus on novel functionalities accessible in the stable branch of the package or via interfaces with other packages. These developments span a wide range of topics in computational chemistry and are presented in thematic sections: electronic structure theory, electronic spectroscopy simulations, analytic gradients and molecular structure optimizations, ab initio molecular dynamics, and other new features. This report offers an overview of the chemical phenomena and processes OpenMolcas can address, while showing that OpenMolcas is an attractive platform for state-of-the-art atomistic computer simulations.
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Affiliation(s)
- Giovanni Li Manni
- Electronic
Structure Theory Department, Max Planck
Institute for Solid State Research, Heisenbergstraße 1, 70569 Stuttgart, Germany
| | - Ignacio Fdez. Galván
- Department
of Chemistry − BMC, Uppsala University, P.O. Box 576, SE-75123 Uppsala, Sweden
| | - Ali Alavi
- Electronic
Structure Theory Department, Max Planck
Institute for Solid State Research, Heisenbergstraße 1, 70569 Stuttgart, Germany
- Yusuf Hamied
Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, United Kingdom
| | - Flavia Aleotti
- Department
of Industrial Chemistry “Toso Montanari”, University of Bologna, 40136 Bologna, Italy
| | - Francesco Aquilante
- Theory and
Simulation of Materials (THEOS) and National Centre for Computational
Design and Discovery of Novel Materials (MARVEL), École Polytechnique Fédérale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland
| | - Jochen Autschbach
- Department
of Chemistry, University at Buffalo, State
University of New York, Buffalo, New York 14260-3000, United States
| | - Davide Avagliano
- Department
of Industrial Chemistry “Toso Montanari”, University of Bologna, 40136 Bologna, Italy
| | - Alberto Baiardi
- ETH Zurich, Laboratory for Physical Chemistry, Vladimir-Prelog-Weg 2, 8093 Zurich, Switzerland
| | - Jie J. Bao
- Department
of Chemistry, Chemical Theory Center, and Minnesota Supercomputing
Institute, University of Minnesota, Minneapolis, Minnesota 55455-0431, United
States
| | - Stefano Battaglia
- Department
of Chemistry − BMC, Uppsala University, P.O. Box 576, SE-75123 Uppsala, Sweden
| | - Letitia Birnoschi
- The Department
of Chemistry, The University of Manchester, M13 9PL, Manchester, U.K.
| | - Alejandro Blanco-González
- Chemistry
Department, Bowling Green State University, Overmann Hall, Bowling Green, Ohio 43403, United States
| | - Sergey I. Bokarev
- Institut
für Physik, Universität Rostock, Albert-Einstein-Str. 23-24, 18059 Rostock, Germany
- Chemistry
Department, School of Natural Sciences, Technical University of Munich, Lichtenbergstr. 4, 85748 Garching, Germany
| | - Ria Broer
- Theoretical
Chemistry, Zernike Institute for Advanced Materials, University of Groningen, Nijenborgh 4, 9747AG Groningen, The Netherlands
| | - Roberto Cacciari
- Dipartimento
di Biotecnologie, Chimica e Farmacia, Università
di Siena, Via A. Moro 2, 53100 Siena, Italy
| | - Paul B. Calio
- Department
of Chemistry, Pritzker School of Molecular Engineering, James Franck
Institute, Chicago Center for Theoretical Chemistry, The University of Chicago, Chicago, Illinois 60637, United States
| | - Rebecca K. Carlson
- Department
of Chemistry, Chemical Theory Center, and Minnesota Supercomputing
Institute, University of Minnesota, Minneapolis, Minnesota 55455-0431, United
States
| | - Rafael Carvalho Couto
- Division
of Theoretical Chemistry and Biology, School of Engineering Sciences
in Chemistry, Biotechnology and Health, KTH Royal Institute of Technology, SE-106 91 Stockholm, Sweden
| | - Luis Cerdán
- Instituto
de Ciencia Molecular, Universitat de València, Catedrático José Beltrán
Martínez n. 2, 46980 Paterna, Spain
- Instituto
de Óptica (IO−CSIC), Consejo
Superior de Investigaciones Científicas, 28006, Madrid, Spain
| | - Liviu F. Chibotaru
- Department
of Chemistry, KU Leuven, Celestijnenlaan 200F, 3001 Leuven, Belgium
| | - Nicholas F. Chilton
- The Department
of Chemistry, The University of Manchester, M13 9PL, Manchester, U.K.
| | | | - Irene Conti
- Department
of Industrial Chemistry “Toso Montanari”, University of Bologna, 40136 Bologna, Italy
| | - Sonia Coriani
- Department
of Chemistry, Technical University of Denmark, Kemitorvet Bldg 207, 2800 Kongens Lyngby, Denmark
| | - Juliana Cuéllar-Zuquin
- Instituto
de Ciencia Molecular, Universitat de València, Catedrático José Beltrán
Martínez n. 2, 46980 Paterna, Spain
| | - Razan E. Daoud
- Dipartimento
di Biotecnologie, Chimica e Farmacia, Università
di Siena, Via A. Moro 2, 53100 Siena, Italy
| | - Nike Dattani
- HPQC Labs, Waterloo, N2T 2K9 Ontario Canada
- HPQC College, Waterloo, N2T 2K9 Ontario Canada
| | - Piero Decleva
- Istituto
Officina dei Materiali IOM-CNR and Dipartimento di Scienze Chimiche
e Farmaceutiche, Università degli
Studi di Trieste, I-34121 Trieste, Italy
| | - Coen de Graaf
- Department
of Physical and Inorganic Chemistry, Universitat
Rovira i Virgili, Tarragona 43007, Spain
- ICREA, Pg. Lluís
Companys 23, 08010 Barcelona, Spain
| | - Mickaël
G. Delcey
- Division
of Theoretical Chemistry and Biology, School of Engineering Sciences
in Chemistry, Biotechnology and Health, KTH Royal Institute of Technology, SE-106 91 Stockholm, Sweden
| | - Luca De Vico
- Dipartimento
di Biotecnologie, Chimica e Farmacia, Università
di Siena, Via A. Moro 2, 53100 Siena, Italy
| | - Werner Dobrautz
- Chalmers
University of Technology, Department of Chemistry
and Chemical Engineering, 41296 Gothenburg, Sweden
| | - Sijia S. Dong
- Department
of Chemistry, Chemical Theory Center, and Minnesota Supercomputing
Institute, University of Minnesota, Minneapolis, Minnesota 55455-0431, United
States
- Department
of Chemistry and Chemical Biology, Department of Physics, and Department
of Chemical Engineering, Northeastern University, Boston, Massachusetts 02115, United States
| | - Rulin Feng
- Department
of Chemistry, University at Buffalo, State
University of New York, Buffalo, New York 14260-3000, United States
- Department
of Chemistry, Fudan University, Shanghai 200433, China
| | - Nicolas Ferré
- Institut
de Chimie Radicalaire (UMR-7273), Aix-Marseille
Univ, CNRS, ICR 13013 Marseille, France
| | | | - Laura Gagliardi
- Department
of Chemistry, Chemical Theory Center, and Minnesota Supercomputing
Institute, University of Minnesota, Minneapolis, Minnesota 55455-0431, United
States
- Department
of Chemistry, Pritzker School of Molecular Engineering, James Franck
Institute, Chicago Center for Theoretical Chemistry, The University of Chicago, Chicago, Illinois 60637, United States
| | - Marco Garavelli
- Department
of Industrial Chemistry “Toso Montanari”, University of Bologna, 40136 Bologna, Italy
| | - Leticia González
- Institute
of Theoretical Chemistry, Faculty of Chemistry, University of Vienna, Währinger Straße 17, A-1090 Vienna, Austria
| | - Yafu Guan
- State Key
Laboratory of Molecular Reaction Dynamics and Center for Theoretical
Computational Chemistry, Dalian Institute
of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, People’s Republic of China
| | - Meiyuan Guo
- SSRL, SLAC National Accelerator Laboratory, Menlo Park, California 94025, United States
| | - Matthew R. Hennefarth
- Department
of Chemistry, Pritzker School of Molecular Engineering, James Franck
Institute, Chicago Center for Theoretical Chemistry, The University of Chicago, Chicago, Illinois 60637, United States
| | - Matthew R. Hermes
- Department
of Chemistry, Chemical Theory Center, and Minnesota Supercomputing
Institute, University of Minnesota, Minneapolis, Minnesota 55455-0431, United
States
- Department
of Chemistry, Pritzker School of Molecular Engineering, James Franck
Institute, Chicago Center for Theoretical Chemistry, The University of Chicago, Chicago, Illinois 60637, United States
| | - Chad E. Hoyer
- Department
of Chemistry, Chemical Theory Center, and Minnesota Supercomputing
Institute, University of Minnesota, Minneapolis, Minnesota 55455-0431, United
States
- Department
of Chemistry, University of Washington, Seattle, Washington 98195, United States
| | - Miquel Huix-Rotllant
- Institut
de Chimie Radicalaire (UMR-7273), Aix-Marseille
Univ, CNRS, ICR 13013 Marseille, France
| | - Vishal Kumar Jaiswal
- Department
of Industrial Chemistry “Toso Montanari”, University of Bologna, 40136 Bologna, Italy
| | - Andy Kaiser
- Institut
für Physik, Universität Rostock, Albert-Einstein-Str. 23-24, 18059 Rostock, Germany
| | - Danil S. Kaliakin
- Chemistry
Department, Bowling Green State University, Overmann Hall, Bowling Green, Ohio 43403, United States
| | - Marjan Khamesian
- Department
of Chemistry − BMC, Uppsala University, P.O. Box 576, SE-75123 Uppsala, Sweden
| | - Daniel S. King
- Department
of Chemistry, Pritzker School of Molecular Engineering, James Franck
Institute, Chicago Center for Theoretical Chemistry, The University of Chicago, Chicago, Illinois 60637, United States
| | - Vladislav Kochetov
- Institut
für Physik, Universität Rostock, Albert-Einstein-Str. 23-24, 18059 Rostock, Germany
| | - Marek Krośnicki
- Institute
of Theoretical Physics and Astrophysics, Faculty of Mathematics, Physics
and Informatics, University of Gdańsk, ul Wita Stwosza 57, 80-952, Gdańsk, Poland
| | | | - Ernst D. Larsson
- Division
of Theoretical Chemistry, Chemical Centre, Lund University, P.O. Box 124, SE-22100, Lund, Sweden
| | - Susi Lehtola
- Molecular
Sciences Software Institute, Blacksburg, Virginia 24061, United States
- Department
of Chemistry, University of Helsinki, P.O. Box 55, FI-00014 University of Helsinki, Finland
| | - Marie-Bernadette Lepetit
- Condensed
Matter Theory Group, Institut Néel, CNRS UPR 2940, 38042 Grenoble, France
- Theory
Group, Institut Laue Langevin, 38042 Grenoble, France
| | - Hans Lischka
- Department
of Chemistry and Biochemistry, Texas Tech
University, Lubbock, Texas 79409-1061, United States
| | - Pablo López Ríos
- Electronic
Structure Theory Department, Max Planck
Institute for Solid State Research, Heisenbergstraße 1, 70569 Stuttgart, Germany
| | - Marcus Lundberg
- Department
of Chemistry − Ångström Laboratory, Uppsala University, SE-75120 Uppsala, Sweden
| | - Dongxia Ma
- Electronic
Structure Theory Department, Max Planck
Institute for Solid State Research, Heisenbergstraße 1, 70569 Stuttgart, Germany
- Department
of Chemistry, Chemical Theory Center, and Minnesota Supercomputing
Institute, University of Minnesota, Minneapolis, Minnesota 55455-0431, United
States
| | - Sebastian Mai
- Institute
of Theoretical Chemistry, Faculty of Chemistry, University of Vienna, Währinger Straße 17, A-1090 Vienna, Austria
| | - Philipp Marquetand
- Institute
of Theoretical Chemistry, Faculty of Chemistry, University of Vienna, Währinger Straße 17, A-1090 Vienna, Austria
| | | | - Francesco Montorsi
- Department
of Industrial Chemistry “Toso Montanari”, University of Bologna, 40136 Bologna, Italy
| | - Maximilian Mörchen
- ETH Zurich, Laboratory for Physical Chemistry, Vladimir-Prelog-Weg 2, 8093 Zurich, Switzerland
| | - Artur Nenov
- Department
of Industrial Chemistry “Toso Montanari”, University of Bologna, 40136 Bologna, Italy
| | - Vu Ha Anh Nguyen
- Department
of Chemistry, National University of Singapore, 3 Science Drive 3, 117543 Singapore
| | - Yoshio Nishimoto
- Graduate
School of Science, Kyoto University, Kyoto 606-8502, Japan
| | - Meagan S. Oakley
- Department
of Chemistry, Chemical Theory Center, and Minnesota Supercomputing
Institute, University of Minnesota, Minneapolis, Minnesota 55455-0431, United
States
| | - Massimo Olivucci
- Chemistry
Department, Bowling Green State University, Overmann Hall, Bowling Green, Ohio 43403, United States
- Dipartimento
di Biotecnologie, Chimica e Farmacia, Università
di Siena, Via A. Moro 2, 53100 Siena, Italy
| | - Markus Oppel
- Institute
of Theoretical Chemistry, Faculty of Chemistry, University of Vienna, Währinger Straße 17, A-1090 Vienna, Austria
| | - Daniele Padula
- Dipartimento
di Biotecnologie, Chimica e Farmacia, Università
di Siena, Via A. Moro 2, 53100 Siena, Italy
| | - Riddhish Pandharkar
- Department
of Chemistry, Chemical Theory Center, and Minnesota Supercomputing
Institute, University of Minnesota, Minneapolis, Minnesota 55455-0431, United
States
- Department
of Chemistry, Pritzker School of Molecular Engineering, James Franck
Institute, Chicago Center for Theoretical Chemistry, The University of Chicago, Chicago, Illinois 60637, United States
| | - Quan Manh Phung
- Department
of Chemistry, Graduate School of Science, Nagoya University, Furo-cho, Chikusa-ku, Nagoya, Aichi 464-8602, Japan
- Institute
of Transformative Bio-Molecules (WPI-ITbM), Nagoya University, Furo-cho, Chikusa-ku, Nagoya, Aichi 464-8601, Japan
| | - Felix Plasser
- Department
of Chemistry, Loughborough University, Loughborough, LE11 3TU, U.K.
| | - Gerardo Raggi
- Department
of Chemistry − BMC, Uppsala University, P.O. Box 576, SE-75123 Uppsala, Sweden
- Quantum
Materials and Software LTD, 128 City Road, London, EC1V 2NX, United Kingdom
| | - Elisa Rebolini
- Scientific
Computing Group, Institut Laue Langevin, 38042 Grenoble, France
| | - Markus Reiher
- ETH Zurich, Laboratory for Physical Chemistry, Vladimir-Prelog-Weg 2, 8093 Zurich, Switzerland
| | - Ivan Rivalta
- Department
of Industrial Chemistry “Toso Montanari”, University of Bologna, 40136 Bologna, Italy
| | - Daniel Roca-Sanjuán
- Instituto
de Ciencia Molecular, Universitat de València, Catedrático José Beltrán
Martínez n. 2, 46980 Paterna, Spain
| | - Thies Romig
- Institut
für Physik, Universität Rostock, Albert-Einstein-Str. 23-24, 18059 Rostock, Germany
| | - Arta Anushirwan Safari
- Electronic
Structure Theory Department, Max Planck
Institute for Solid State Research, Heisenbergstraße 1, 70569 Stuttgart, Germany
| | - Aitor Sánchez-Mansilla
- Department
of Physical and Inorganic Chemistry, Universitat
Rovira i Virgili, Tarragona 43007, Spain
| | - Andrew M. Sand
- Department
of Chemistry, Chemical Theory Center, and Minnesota Supercomputing
Institute, University of Minnesota, Minneapolis, Minnesota 55455-0431, United
States
- Department
of Chemistry and Biochemistry, Butler University, Indianapolis, Indiana 46208, United States
| | - Igor Schapiro
- Institute
of Chemistry, The Hebrew University of Jerusalem, Jerusalem 91904, Israel
| | - Thais R. Scott
- Department
of Chemistry, Chemical Theory Center, and Minnesota Supercomputing
Institute, University of Minnesota, Minneapolis, Minnesota 55455-0431, United
States
- Department
of Chemistry, Pritzker School of Molecular Engineering, James Franck
Institute, Chicago Center for Theoretical Chemistry, The University of Chicago, Chicago, Illinois 60637, United States
- Department
of Chemistry, University of California, Irvine, California 92697, United States
| | - Javier Segarra-Martí
- Instituto
de Ciencia Molecular, Universitat de València, Catedrático José Beltrán
Martínez n. 2, 46980 Paterna, Spain
| | - Francesco Segatta
- Department
of Industrial Chemistry “Toso Montanari”, University of Bologna, 40136 Bologna, Italy
| | - Dumitru-Claudiu Sergentu
- Department
of Chemistry, University at Buffalo, State
University of New York, Buffalo, New York 14260-3000, United States
- Laboratory
RA-03, RECENT AIR, A. I. Cuza University of Iaşi, RA-03 Laboratory (RECENT AIR), Iaşi 700506, Romania
| | - Prachi Sharma
- Department
of Chemistry, Chemical Theory Center, and Minnesota Supercomputing
Institute, University of Minnesota, Minneapolis, Minnesota 55455-0431, United
States
| | - Ron Shepard
- Chemical
Sciences and Engineering Division, Argonne
National Laboratory, Lemont, Illinois 60439, USA
| | - Yinan Shu
- Department
of Chemistry, Chemical Theory Center, and Minnesota Supercomputing
Institute, University of Minnesota, Minneapolis, Minnesota 55455-0431, United
States
| | - Jakob K. Staab
- The Department
of Chemistry, The University of Manchester, M13 9PL, Manchester, U.K.
| | - Tjerk P. Straatsma
- National
Center for Computational Sciences, Oak Ridge
National Laboratory, Oak Ridge, Tennessee 37831-6373, United States
- Department
of Chemistry and Biochemistry, University
of Alabama, Tuscaloosa, Alabama 35487-0336, United States
| | | | - Bruno Nunes Cabral Tenorio
- Department
of Chemistry, Technical University of Denmark, Kemitorvet Bldg 207, 2800 Kongens Lyngby, Denmark
| | - Donald G. Truhlar
- Department
of Chemistry, Chemical Theory Center, and Minnesota Supercomputing
Institute, University of Minnesota, Minneapolis, Minnesota 55455-0431, United
States
| | - Liviu Ungur
- Department
of Chemistry, National University of Singapore, 3 Science Drive 3, 117543 Singapore
| | - Morgane Vacher
- Nantes
Université, CNRS, CEISAM, UMR 6230, F-44000 Nantes, France
| | - Valera Veryazov
- Division
of Theoretical Chemistry, Chemical Centre, Lund University, P.O. Box 124, SE-22100, Lund, Sweden
| | - Torben Arne Voß
- Institut
für Physik, Universität Rostock, Albert-Einstein-Str. 23-24, 18059 Rostock, Germany
| | - Oskar Weser
- Electronic
Structure Theory Department, Max Planck
Institute for Solid State Research, Heisenbergstraße 1, 70569 Stuttgart, Germany
| | - Dihua Wu
- Department
of Chemistry, Chemical Theory Center, and Minnesota Supercomputing
Institute, University of Minnesota, Minneapolis, Minnesota 55455-0431, United
States
| | - Xuchun Yang
- Chemistry
Department, Bowling Green State University, Overmann Hall, Bowling Green, Ohio 43403, United States
| | - David Yarkony
- Department
of Chemistry, Johns Hopkins University, Baltimore, Maryland 21218, United States
| | - Chen Zhou
- Department
of Chemistry, Chemical Theory Center, and Minnesota Supercomputing
Institute, University of Minnesota, Minneapolis, Minnesota 55455-0431, United
States
| | - J. Patrick Zobel
- Institute
of Theoretical Chemistry, Faculty of Chemistry, University of Vienna, Währinger Straße 17, A-1090 Vienna, Austria
| | - Roland Lindh
- Department
of Chemistry − BMC, Uppsala University, P.O. Box 576, SE-75123 Uppsala, Sweden
- Uppsala
Center for Computational Chemistry (UC3), Uppsala University, PO Box 576, SE-751 23 Uppsala. Sweden
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Ng WP, Liang Q, Yang J. Low-Data Deep Quantum Chemical Learning for Accurate MP2 and Coupled-Cluster Correlations. J Chem Theory Comput 2023; 19:5439-5449. [PMID: 37506400 DOI: 10.1021/acs.jctc.3c00518] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/30/2023]
Abstract
Accurate ab initio prediction of electronic energies is very expensive for macromolecules by explicitly solving post-Hartree-Fock equations. We here exploit the physically justified local correlation feature in a compact basis of small molecules and construct an expressive low-data deep neural network (dNN) model to obtain machine-learned electron correlation energies on par with MP2 and CCSD levels of theory for more complex molecules and different datasets that are not represented in the training set. We show that our dNN-powered model is data efficient and makes highly transferable predictions across alkanes of various lengths, organic molecules with non-covalent and biomolecular interactions, as well as water clusters of different sizes and morphologies. In particular, by training 800 (H2O)8 clusters with the local correlation descriptors, accurate MP2/cc-pVTZ correlation energies up to (H2O)128 can be predicted with a small random error within chemical accuracy from exact values, while a majority of prediction deviations are attributed to an intrinsically systematic error. Our results reveal that an extremely compact local correlation feature set, which is poor for any direct post-Hartree-Fock calculations, has however a prominent advantage in reserving important electron correlation patterns for making accurate transferable predictions across distinct molecular compositions, bond types, and geometries.
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Affiliation(s)
- Wai-Pan Ng
- Department of Chemistry, The University of Hong Kong, Hong Kong 999077, P. R. China
- Hong Kong Quantum AI Lab Limited, Hong Kong 999077, P. R. China
| | - Qiujiang Liang
- Department of Chemistry, The University of Hong Kong, Hong Kong 999077, P. R. China
| | - Jun Yang
- Department of Chemistry, The University of Hong Kong, Hong Kong 999077, P. R. China
- Hong Kong Quantum AI Lab Limited, Hong Kong 999077, P. R. China
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13
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Kaufold B, Chintala N, Pandeya P, Dong SS. Automated Active Space Selection with Dipole Moments. J Chem Theory Comput 2023; 19:2469-2483. [PMID: 37040135 PMCID: PMC10629219 DOI: 10.1021/acs.jctc.2c01128] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Indexed: 04/12/2023]
Abstract
Multireference calculations can provide accurate information of systems with strong correlation, which have increasing importance in the development of new molecules and materials. However, selecting a suitable active space for multireference calculations is nontrivial, and the selection of an unsuitable active space can sometimes lead to results that are not physically meaningful. Active space selection often requires significant human input, and the selection that leads to reasonable results often goes beyond chemical intuition. In this work, we have developed and evaluated two protocols for automated selection of the active space for multireference calculations based on a simple physical observable, the dipole moment, for molecules with nonzero ground-state dipole moments. One protocol is based on the ground-state dipole moment, and the other is based on the excited-state dipole moments. To evaluate the protocols, we constructed a dataset of 1275 active spaces from 25 molecules, each with 51 active space sizes considered, and have mapped out the relationship between the active space, dipole moments, and vertical excitation energies. We have demonstrated that, within this dataset, our protocols allow one to choose among a number of accessible active spaces one that is likely to give reasonable vertical excitation energies, especially for the first three excitations, with no parameters manually decided by the user. We show that, with large active spaces removed from consideration, the accuracy is similar and the time-to-solution can be reduced by more than 10 fold. We also show that the protocols can be applied to potential energy surface scans and determining the spin states of transition metal oxides.
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Affiliation(s)
- Benjamin
W. Kaufold
- Department
of Chemistry and Chemical Biology, Northeastern
University, Boston, Massachusetts 02115, United States
| | - Nithin Chintala
- Department
of Chemistry and Chemical Biology, Northeastern
University, Boston, Massachusetts 02115, United States
| | - Pratima Pandeya
- Department
of Chemistry and Chemical Biology, Northeastern
University, Boston, Massachusetts 02115, United States
- The
Institute for Experiential AI, Northeastern
University, Boston, Massachusetts 02115, United States
| | - Sijia S. Dong
- Department
of Chemistry and Chemical Biology, Northeastern
University, Boston, Massachusetts 02115, United States
- Department
of Physics and Department of Chemical Engineering, Northeastern University, Boston, Massachusetts 02115, United States
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14
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Mutual information prediction for strongly correlated systems. Chem Phys Lett 2023. [DOI: 10.1016/j.cplett.2023.140297] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
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15
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King D, Hermes MR, Truhlar DG, Gagliardi L. Large-Scale Benchmarking of Multireference Vertical-Excitation Calculations via Automated Active-Space Selection. J Chem Theory Comput 2022; 18:6065-6076. [PMID: 36112354 PMCID: PMC9558375 DOI: 10.1021/acs.jctc.2c00630] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Indexed: 11/29/2022]
Abstract
We have calculated state-averaged complete-active-space self-consistent-field (SA-CASSCF), multiconfiguration pair-density functional theory (MC-PDFT), hybrid MC-PDFT (HMC-PDFT), and n-electron valence state second-order perturbation theory (NEVPT2) excitation energies with the approximate pair coefficient (APC) automated active-space selection scheme for the QUESTDB benchmark database of 542 vertical excitation energies. We eliminated poor active spaces (20-40% of calculations) by applying a threshold to the SA-CASSCF absolute error. With the remaining calculations, we find that NEVPT2 performance is significantly impacted by the size of the basis set the wave functions are converged in, regardless of the quality of their description, which is a problem absent in MC-PDFT. Additionally, we find that HMC-PDFT is a significant improvement over MC-PDFT with the translated PBE (tPBE) density functional and that it performs about as well as NEVPT2 and second-order coupled cluster on a set of 373 excitations in the QUESTDB database. We optimized the percentage of SA-CASSCF energy to include in HMC-PDFT when using the tPBE on-top functional, and we find the 25% value used in tPBE0 to be optimal. This work is by far the largest benchmarking of MC-PDFT and HMC-PDFT to date, and the data produced in this work are useful as a validation of HMC-PDFT and of the APC active-space selection scheme. We have made all the wave functions produced in this work (orbitals and CI vectors) available to the public and encourage the community to utilize this data as a tool in the development of further multireference model chemistries.
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Affiliation(s)
- Daniel
S. King
- Department
of Chemistry, University of Chicago, Chicago Illinois 60637, United States
| | - Matthew R. Hermes
- Department
of Chemistry, University of Chicago, Chicago Illinois 60637, United States
| | - Donald G. Truhlar
- Department
of Chemistry, Chemical Theory Center, and Minnesota Supercomputng
Institute, University of Minnesota, Minneapolis Minnesota 55455-0431, United States
| | - Laura Gagliardi
- Department
of Chemistry, Pritzker School of Molecular Engineering, James Franck
Institute, Chicago Center for Theoretical Chemistry, University of Chicago, Chicago Illinois 60637, United States
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16
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Sauza-de la Vega A, Pandharkar R, Stroscio GD, Sarkar A, Truhlar DG, Gagliardi L. Multiconfiguration Pair-Density Functional Theory for Chromium(IV) Molecular Qubits. JACS AU 2022; 2:2029-2037. [PMID: 36186551 PMCID: PMC9516709 DOI: 10.1021/jacsau.2c00306] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/17/2022] [Revised: 08/19/2022] [Accepted: 08/19/2022] [Indexed: 05/30/2023]
Abstract
Pseudotetrahedral organometallic complexes containing chromium(IV) and aryl ligands have been experimentally identified as promising molecular qubit candidates. Here we present a computational protocol based on multiconfiguration pair-density functional theory for computing singlet-triplet gaps and zero-field splitting (ZFS) parameters in Cr(IV) aryl complexes. We find that two multireference methods, multistate complete active space second-order perturbation theory (MS-CASPT2) and hybrid multistate pair-density functional theory (HMS-PDFT), perform better than Kohn-Sham density functional theory for singlet-triplet gaps. Despite the very small values of the ZFS parameters, both multireference methods performed qualitatively well. MS-CASPT2 and HMS-PDFT performed particularly well for predicting the trend in the ratio of the rhombic and axial ZFS parameters, |E/D|. We have also investigated the dependence and sensitivity of the calculated ZFS parameters on the active space and the molecular geometry. The methodologies outlined here can guide future prediction of ZFS parameters in molecular qubit candidates.
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Affiliation(s)
- Arturo Sauza-de la Vega
- Department
of Chemistry, Pritzker School of Molecular Engineering, James Franck
Institute, Chicago Center for Theoretical Chemistry, University of Chicago, Chicago, Illinois 60637, United States
| | - Riddhish Pandharkar
- Department
of Chemistry, Pritzker School of Molecular Engineering, James Franck
Institute, Chicago Center for Theoretical Chemistry, University of Chicago, Chicago, Illinois 60637, United States
- Argonne
National Laboratory, Lemont, Illinois 60439, United States
| | - Gautam D. Stroscio
- Department
of Chemistry, Pritzker School of Molecular Engineering, James Franck
Institute, Chicago Center for Theoretical Chemistry, University of Chicago, Chicago, Illinois 60637, United States
| | - Arup Sarkar
- Department
of Chemistry, Pritzker School of Molecular Engineering, James Franck
Institute, Chicago Center for Theoretical Chemistry, University of Chicago, Chicago, Illinois 60637, United States
| | - Donald G. Truhlar
- Department
of Chemistry, Chemical Theory Center, and Minnesota Supercomputing
Institute, University of Minnesota, Minneapolis, Minnesota 55455−0431, United States
| | - Laura Gagliardi
- Department
of Chemistry, Pritzker School of Molecular Engineering, James Franck
Institute, Chicago Center for Theoretical Chemistry, University of Chicago, Chicago, Illinois 60637, United States
- Argonne
National Laboratory, Lemont, Illinois 60439, United States
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17
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Duan C, Ladera AJ, Liu JCL, Taylor MG, Ariyarathna IR, Kulik HJ. Exploiting Ligand Additivity for Transferable Machine Learning of Multireference Character across Known Transition Metal Complex Ligands. J Chem Theory Comput 2022; 18:4836-4845. [PMID: 35834742 DOI: 10.1021/acs.jctc.2c00468] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Accurate virtual high-throughput screening (VHTS) of transition metal complexes (TMCs) remains challenging due to the possibility of high multireference (MR) character that complicates property evaluation. We compute MR diagnostics for over 5,000 ligands present in previously synthesized octahedral mononuclear transition metal complexes in the Cambridge Structural Database (CSD). To accomplish this task, we introduce an iterative approach for consistent ligand charge assignment for ligands in the CSD. Across this set, we observe that the MR character correlates linearly with the inverse value of the averaged bond order over all bonds in the molecule. We then demonstrate that ligand additivity of the MR character holds in TMCs, which suggests that the TMC MR character can be inferred from the sum of the MR character of the ligands. Encouraged by this observation, we leverage ligand additivity and develop a ligand-derived machine learning representation to train neural networks to predict the MR character of TMCs from properties of the constituent ligands. This approach yields models with excellent performance and superior transferability to unseen ligand chemistry and compositions.
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Affiliation(s)
- Chenru Duan
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States.,Department of Chemistry, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Adriana J Ladera
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Julian C-L Liu
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Michael G Taylor
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Isuru R Ariyarathna
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Heather J Kulik
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
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18
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Wang Y, Seritan S, Lahana D, Ford JE, Valentini A, Hohenstein EG, Martínez TJ. InteraChem: Exploring Excited States in Virtual Reality with Ab Initio Interactive Molecular Dynamics. J Chem Theory Comput 2022; 18:3308-3317. [PMID: 35649124 DOI: 10.1021/acs.jctc.2c00005] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
Abstract
InteraChem is an ab initio interactive molecular dynamics (AI-IMD) visualizer that leverages recent advances in virtual reality hardware and software, as well as the graphical processing unit (GPU)-accelerated TeraChem electronic structure package, in order to render quantum chemistry in real time. We introduce the exploration of electronically excited states via AI-IMD using the floating occupation molecular orbital-complete active space configuration interaction method. The optimization tools in InteraChem enable identification of excited state minima as well as minimum energy conical intersections for further characterization of excited state chemistry in small- to medium-sized systems. We demonstrate that finite-temperature Hartree-Fock theory is an efficient method to perform ground state AI-IMD. InteraChem allows users to track electronic properties such as molecular orbitals and bond order in real time, resulting in an interactive visualization tool that aids in the interpretation of excited state chemistry data and makes quantum chemistry more accessible for both research and educational purposes.
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Affiliation(s)
- Yuanheng Wang
- Department of Chemistry and The PULSE Institute, Stanford University, Stanford, California 94305, United States.,SLAC National Accelerator Laboratory, 2575 Sand Hill Road, Menlo Park, California 94025, United States
| | - Stefan Seritan
- Department of Chemistry and The PULSE Institute, Stanford University, Stanford, California 94305, United States.,SLAC National Accelerator Laboratory, 2575 Sand Hill Road, Menlo Park, California 94025, United States
| | - Dean Lahana
- Department of Chemistry and The PULSE Institute, Stanford University, Stanford, California 94305, United States.,SLAC National Accelerator Laboratory, 2575 Sand Hill Road, Menlo Park, California 94025, United States
| | - Jason E Ford
- Department of Chemistry and The PULSE Institute, Stanford University, Stanford, California 94305, United States.,SLAC National Accelerator Laboratory, 2575 Sand Hill Road, Menlo Park, California 94025, United States
| | - Alessio Valentini
- Department of Chemistry and The PULSE Institute, Stanford University, Stanford, California 94305, United States.,SLAC National Accelerator Laboratory, 2575 Sand Hill Road, Menlo Park, California 94025, United States
| | - Edward G Hohenstein
- Department of Chemistry and The PULSE Institute, Stanford University, Stanford, California 94305, United States.,SLAC National Accelerator Laboratory, 2575 Sand Hill Road, Menlo Park, California 94025, United States
| | - Todd J Martínez
- Department of Chemistry and The PULSE Institute, Stanford University, Stanford, California 94305, United States.,SLAC National Accelerator Laboratory, 2575 Sand Hill Road, Menlo Park, California 94025, United States
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19
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Nandy A, Duan C, Kulik HJ. Audacity of huge: overcoming challenges of data scarcity and data quality for machine learning in computational materials discovery. Curr Opin Chem Eng 2022. [DOI: 10.1016/j.coche.2021.100778] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
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20
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Duan C, Chu DBK, Nandy A, Kulik HJ. Detection of multi-reference character imbalances enables a transfer learning approach for virtual high throughput screening with coupled cluster accuracy at DFT cost. Chem Sci 2022; 13:4962-4971. [PMID: 35655882 PMCID: PMC9067623 DOI: 10.1039/d2sc00393g] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2022] [Accepted: 04/04/2022] [Indexed: 01/08/2023] Open
Abstract
Appropriately identifying and treating molecules and materials with significant multi-reference (MR) character is crucial for achieving high data fidelity in virtual high-throughput screening (VHTS). Despite development of numerous MR diagnostics, the extent to which a single value of such a diagnostic indicates the MR effect on a chemical property prediction is not well established. We evaluate MR diagnostics for over 10 000 transition-metal complexes (TMCs) and compare to those for organic molecules. We observe that only some MR diagnostics are transferable from one chemical space to another. By studying the influence of MR character on chemical properties (i.e., MR effect) that involve multiple potential energy surfaces (i.e., adiabatic spin splitting, ΔE H-L, and ionization potential, IP), we show that differences in MR character are more important than the cumulative degree of MR character in predicting the magnitude of an MR effect. Motivated by this observation, we build transfer learning models to predict CCSD(T)-level adiabatic ΔE H-L and IP from lower levels of theory. By combining these models with uncertainty quantification and multi-level modeling, we introduce a multi-pronged strategy that accelerates data acquisition by at least a factor of three while achieving coupled cluster accuracy (i.e., to within 1 kcal mol-1 MAE) for robust VHTS.
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Affiliation(s)
- Chenru Duan
- Department of Chemical Engineering, Massachusetts Institute of Technology Cambridge MA 02139 USA
- Department of Chemistry, Massachusetts Institute of Technology Cambridge MA 02139 USA
| | - Daniel B K Chu
- Department of Chemical Engineering, Massachusetts Institute of Technology Cambridge MA 02139 USA
| | - Aditya Nandy
- Department of Chemical Engineering, Massachusetts Institute of Technology Cambridge MA 02139 USA
- Department of Chemistry, Massachusetts Institute of Technology Cambridge MA 02139 USA
| | - Heather J Kulik
- Department of Chemical Engineering, Massachusetts Institute of Technology Cambridge MA 02139 USA
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21
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Vitillo JG, Cramer CJ, Gagliardi L. Multireference Methods are Realistic and Useful Tools for Modeling Catalysis. Isr J Chem 2022. [DOI: 10.1002/ijch.202100136] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Affiliation(s)
- Jenny G. Vitillo
- Department of Science and High Technology and INSTM Università degli Studi dell'Insubria Via Valleggio 9 I-22100 Como Italy
| | - Christopher J. Cramer
- Underwriters Laboratories Inc. 333 Pfingsten Road Northbrook Illinois 60602 United States
| | - Laura Gagliardi
- Department of Chemistry Pritzker School of Molecular Engineering James Franck Institute University of Chicago Chicago Illinois 60637 United States
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22
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Soto J, Algarra M. Electronic Structure of Nitrobenzene: A Benchmark Example of the Accuracy of the Multi-State CASPT2 Theory. J Phys Chem A 2021; 125:9431-9437. [PMID: 34677962 PMCID: PMC8573753 DOI: 10.1021/acs.jpca.1c04595] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
![]()
The electronic structure
of nitrobenzene (C6H5NO2) has been
studied by means of the complete active
space self-consistent field (CASSCF) and multi-state second-order
perturbation (MS-CASPT2) methods. To this end, an active space of
20 electrons distributed in 17 orbitals has been selected to construct
the reference wave function. In this work, we have calculated the
vertical excitation energies and the energy barrier for the dissociation
of the molecule on the ground state into phenyl and nitrogen dioxide.
After applying the corresponding vibrational corrections to the electronic
energies, it is demonstrated that the MS-CASPT2//CASSCF values obtained
in this work yield an excellent agreement between calculated and experimental
data. In addition, other active spaces of lower size have been applied
to the system in order to check the active space dependence in the
results.
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Affiliation(s)
- Juan Soto
- Department of Physical Chemistry, Faculty of Science, University of Málaga, Málaga 29071, Spain
| | - Manuel Algarra
- Department of Inorganic Chemistry, Faculty of Science, University of Málaga, Málaga 29071, Spain
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23
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Golub P, Antalik A, Veis L, Brabec J. Machine Learning-Assisted Selection of Active Spaces for Strongly Correlated Transition Metal Systems. J Chem Theory Comput 2021; 17:6053-6072. [PMID: 34570505 DOI: 10.1021/acs.jctc.1c00235] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Active space quantum chemical methods could provide very accurate description of strongly correlated electronic systems, which is of tremendous value for natural sciences. The proper choice of the active space is crucial but a nontrivial task. In this article, we present a neural network-based approach for automatic selection of active spaces, focused on transition metal systems. The training set has been formed from artificial systems composed of one transition metal and various ligands, on which we have performed the density matrix renormalization group and calculated the single-site entropy. On the selected set of systems, ranging from small benchmark molecules up to larger challenging systems involving two metallic centers, we demonstrate that our machine learning models could predict the active space orbitals with reasonable accuracy. We also tested the transferability on out-of-the-model systems, including bimetallic complexes and complexes with ligands, which were not involved in the training set. Also, we tested the correctness of the automatically selected active spaces on a Fe(II)-porphyrin model, where we studied the lowest states at the DMRG level and compared the energy difference between spin states or the energy difference between conformations of ferrocene with recent studies.
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Affiliation(s)
- Pavlo Golub
- J. Heyrovsky Institute of Physical Chemistry, v.v.i., Academy of Sciences of the Czech Republic, Dolejskova 3, 18223 Prague 8, Czech Republic
| | - Andrej Antalik
- J. Heyrovsky Institute of Physical Chemistry, v.v.i., Academy of Sciences of the Czech Republic, Dolejskova 3, 18223 Prague 8, Czech Republic
| | - Libor Veis
- J. Heyrovsky Institute of Physical Chemistry, v.v.i., Academy of Sciences of the Czech Republic, Dolejskova 3, 18223 Prague 8, Czech Republic
| | - Jiri Brabec
- J. Heyrovsky Institute of Physical Chemistry, v.v.i., Academy of Sciences of the Czech Republic, Dolejskova 3, 18223 Prague 8, Czech Republic
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24
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Paz ASP, Baleeva NS, Glover WJ. Active orbital preservation for multiconfigurational self-consistent field. J Chem Phys 2021; 155:071103. [PMID: 34418944 DOI: 10.1063/5.0058673] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022] Open
Abstract
We introduce Active Orbital Preservation for Multiconfigurational Self-Consistent Field (AOP-MCSCF), an automated approach to improving the consistency of active space orbitals over multiple molecular configurations. Our approach is based on maximum overlap with a reference set of active space orbitals taken from a single geometry of a chromophore in the gas phase and can be used to automatically preserve the appropriate orbitals of the chromophore across multiple thermally sampled configurations, even when the chromophore is solvated by quantum-mechanically treated water molecules. In particular, using the singular value decomposition of a Molecular Orbital (MO) overlap matrix between the system and reference, we rotate the MOs of the system to align with the reference active space orbitals and use the resulting rotated orbitals as an initial guess to a MCSCF calculation. We demonstrate the approach on aqueous p-hydroxybenzylidene-imidazolinone (HBI) and find that AOP-MCSCF converges to the "correct" orbitals for over 90% of 3000 thermally sampled configurations. In addition, we compute the linear absorption spectrum and find excellent agreement with new experimental measurements up to 5.4 eV (230 nm). We show that electrostatic contributions to the solvation energy of HBI largely explain the observed state-dependent solvatochromism.
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Affiliation(s)
- Amiel S P Paz
- NYU Shanghai, 1555 Century Avenue, Shanghai 200122, China
| | - Nadezhda S Baleeva
- Institute of Bioorganic Chemistry, Russian Academy of Sciences, Miklukho-Maklaya 16/10, Moscow 117997, Russia
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25
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King DS, Truhlar DG, Gagliardi L. Machine-Learned Energy Functionals for Multiconfigurational Wave Functions. J Phys Chem Lett 2021; 12:7761-7767. [PMID: 34374555 DOI: 10.1021/acs.jpclett.1c02042] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
We introduce multiconfiguration data-driven functional methods (MC-DDFMs), a group of methods which aim to correct the total or classical energy of a qualitatively accurate multiconfigurational wave function using a machine-learned functional of some featurization of the wave function such as its density, on-top density, or both. On a data set of carbene singlet-triplet energy splittings, we show that MC-DDFMs are able to achieve near-benchmark performance on systems not used for training with a robust degree of active-space independence. Beyond demonstrating that the density and on-top density hold the information necessary to correct the singlet-triplet energy splittings of multiconfigurational wave functions, this approach shows great promise for the development of functionals for MC-PDFT because corrections to the classical energy appear to be more transferable to types of molecules not included in the training data than corrections to total energies such as those yielded by CASSCF or NEVPT2.
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Affiliation(s)
- Daniel S King
- Department of Chemistry, University of Chicago, Chicago, Illinois, United States
| | - Donald G Truhlar
- Department of Chemistry, University of Minnesota, Minneapolis, Minnesota, United States
| | - Laura Gagliardi
- Department of Chemistry, Pritzker School of Molecular Engineering, James Franck Institute, Chicago Center for Theoretical Chemistry, University of Chicago, Chicago, Illinois, United States
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