1
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Wen X, Boyn JN, Martirez JMP, Zhao Q, Carter EA. Strategies to Obtain Reliable Energy Landscapes from Embedded Multireference Correlated Wavefunction Methods for Surface Reactions. J Chem Theory Comput 2024. [PMID: 39004994 DOI: 10.1021/acs.jctc.4c00558] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/16/2024]
Abstract
Embedded correlated wavefunction (ECW) theory is a powerful tool for studying ground- and excited-state reaction mechanisms and associated energetics in heterogeneous catalysis. Several factors are important to obtaining reliable ECW energies, critically the construction of consistent active spaces (ASs) along reaction pathways when using a multireference correlated wavefunction (CW) method that relies on a subset of orbital spaces in the configuration interaction expansion to account for static electron correlation, e.g., complete AS self-consistent field theory, in addition to the adequate partitioning of the system into a cluster and environment, as well as the choice of a suitable basis set and number of states included in excited-state simulations. Here, we conducted a series of systematic studies to develop best-practice guidelines for ground- and excited-state ECW theory simulations, utilizing the decomposition of NH3 on Pd(111) as an example. We determine that ECW theory results are relatively insensitive to cluster size, the aug-cc-pVDZ basis set provides an adequate compromise between computational complexity and accuracy, and that a fixed-clean-surface approximation holds well for the derivation of the embedding potential. Additionally, we demonstrate that a merging approach, which involves generating ASs from the molecular fragments at each configuration, is preferable to a creeping approach, which utilizes ASs from adjacent structures as an initial guess, for the generation of consistent potential energy curves involving open-d-shell metal surfaces, and, finally, we show that it is essential to include bands of excited states in their entirety when simulating excited-state reaction pathways.
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Affiliation(s)
- Xuelan Wen
- Department of Mechanical and Aerospace Engineering, Princeton University, Princeton, New Jersey 08544-5263, United States
| | - Jan-Niklas Boyn
- Department of Mechanical and Aerospace Engineering, Princeton University, Princeton, New Jersey 08544-5263, United States
| | - John Mark P Martirez
- Princeton Plasma Physics Laboratory, Princeton, New Jersey 08540-6655, United States
| | - Qing Zhao
- Department of Mechanical and Aerospace Engineering, Princeton University, Princeton, New Jersey 08544-5263, United States
| | - Emily A Carter
- Department of Mechanical and Aerospace Engineering, Princeton University, Princeton, New Jersey 08544-5263, United States
- Princeton Plasma Physics Laboratory, Princeton, New Jersey 08540-6655, United States
- Andlinger Center for Energy and the Environment, Princeton University, Princeton, New Jersey 08544-5263, United States
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2
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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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3
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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: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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4
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Clinton L, Cubitt T, Flynn B, Gambetta FM, Klassen J, Montanaro A, Piddock S, Santos RA, Sheridan E. Towards near-term quantum simulation of materials. Nat Commun 2024; 15:211. [PMID: 38267424 PMCID: PMC10808561 DOI: 10.1038/s41467-023-43479-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Accepted: 11/10/2023] [Indexed: 01/26/2024] Open
Abstract
Determining the ground and excited state properties of materials is considered one of the most promising applications of quantum computers. On near-term hardware, the limiting constraint on such simulations is the requisite circuit depths and qubit numbers, which currently lie well beyond near-term capabilities. Here we develop a quantum algorithm which reduces the estimated cost of material simulations. For example, we obtain a circuit depth improvement by up to 6 orders of magnitude for a Trotter layer of time-dynamics simulation in the transition-metal oxide SrVO3 compared with the best previous quantum algorithms. We achieve this by introducing a collection of connected techniques, including highly localised and physically compact representations of materials Hamiltonians in the Wannier basis, a hybrid fermion-to-qubit mapping, and an efficient circuit compiler. Combined together, these methods leverage locality of materials Hamiltonians and result in a design that generates quantum circuits with depth independent of the system's size. Although the requisite resources for the quantum simulation of materials are still beyond current hardware, our results show that realistic simulation of specific properties may be feasible without necessarily requiring fully scalable, fault-tolerant quantum computers, providing quantum algorithm design incorporates deeper understanding of the target materials and applications.
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5
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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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6
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Mehendale SG, Peng B, Govind N, Alexeev Y. Exploring Parameter Redundancy in the Unitary Coupled-Cluster Ansätze for Hybrid Variational Quantum Computing. J Phys Chem A 2023; 127:4526-4537. [PMID: 37193645 DOI: 10.1021/acs.jpca.3c00550] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/18/2023]
Abstract
One of the commonly used chemically inspired approaches in variational quantum computing is the unitary coupled-cluster (UCC) ansätze. Despite being a systematic way of approaching the exact limit, the number of parameters in the standard UCC ansätze exhibits unfavorable scaling with respect to the system size, hindering its practical use on near-term quantum devices. Efforts have been taken to propose some variants of the UCC ansätze with better scaling. In this paper, we explore the parameter redundancy in the preparation of unitary coupled-cluster singles and doubles (UCCSD) ansätze employing spin-adapted formulation, small amplitude filtration, and entropy-based orbital selection approaches. Numerical results of using our approach on some small molecules have exhibited a significant cost reduction in the number of parameters to be optimized and in the time to convergence compared with conventional UCCSD-VQE simulations. We also discuss the potential application of some machine learning techniques in further exploring the parameter redundancy, providing a possible direction for future studies.
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Affiliation(s)
- Shashank G Mehendale
- Indian Institute of Science Education and Research (IISER), Kolkata, West Bengal 741246, India
| | - Bo Peng
- Physical and Computational Sciences Directorate, Pacific Northwest National Laboratory, Richland, Washington 99354, United States
| | - Niranjan Govind
- Physical and Computational Sciences Directorate, Pacific Northwest National Laboratory, Richland, Washington 99354, United States
| | - Yuri Alexeev
- Computational Science Division, Argonne National Laboratory, Lemont, Illinois 60439, United States
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7
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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: 3.0] [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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8
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Cytter Y, Nandy A, Duan C, Kulik HJ. Insights into the deviation from piecewise linearity in transition metal complexes from supervised machine learning models. Phys Chem Chem Phys 2023; 25:8103-8116. [PMID: 36876903 DOI: 10.1039/d3cp00258f] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/25/2023]
Abstract
Virtual high-throughput screening (VHTS) and machine learning (ML) with density functional theory (DFT) suffer from inaccuracies from the underlying density functional approximation (DFA). Many of these inaccuracies can be traced to the lack of derivative discontinuity that leads to a curvature in the energy with electron addition or removal. Over a dataset of nearly one thousand transition metal complexes typical of VHTS applications, we computed and analyzed the average curvature (i.e., deviation from piecewise linearity) for 23 density functional approximations spanning multiple rungs of "Jacob's ladder". While we observe the expected dependence of the curvatures on Hartree-Fock exchange, we note limited correlation of curvature values between different rungs of "Jacob's ladder". We train ML models (i.e., artificial neural networks or ANNs) to predict the curvature and the associated frontier orbital energies for each of these 23 functionals and then interpret differences in curvature among the different DFAs through analysis of the ML models. Notably, we observe spin to play a much more important role in determining the curvature of range-separated and double hybrids in comparison to semi-local functionals, explaining why curvature values are weakly correlated between these and other families of functionals. Over a space of 187.2k hypothetical compounds, we use our ANNs to pinpoint DFAs for which representative transition metal complexes have near-zero curvature with low uncertainty, demonstrating an approach to accelerate screening of complexes with targeted optical gaps.
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Affiliation(s)
- Yael Cytter
- 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
| | - 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
| | - Heather J Kulik
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.,Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
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9
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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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10
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King DS, 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] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [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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11
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Leszczyk A, Dome T, Tecmer P, Kedziera D, Boguslawski K. Resolving the π-assisted U-N σ f-bond formation using quantum information theory. Phys Chem Chem Phys 2022; 24:21296-21307. [PMID: 36043327 DOI: 10.1039/d2cp03377a] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
We model the potential energy profiles of the UO2 (NCO)Cl2- → NUOCl2- + CO2 reaction pathway [Y. Gong, V. Vallet, M. del Carmen Michelini, D. Rios and J. K. Gibson, J. Phys. Chem. A, 2014, 118, 325-330] using different pair coupled-cluster doubles (pCCD) methods. Specifically, we focus on pCCD and pCCD-tailored coupled cluster models in predicting relative energies for the various intermediates and transition states along the reaction coordinate. Furthermore, we augment our study on energetics with an orbital-pair correlation analysis of the complete reaction pathway that features two distinct paths. Our analysis of orbital correlations sheds new light on the formation and breaking of respective bonds between the uranium, oxygen, and nitrogen atoms along the reaction coordinates where the "yl" bond is broken and a nitrido compound formed. Specifically, the strengthening of the U-N σf-bond is assisted by a π-type interaction that is delocalized over the C-N-U backbone of the UO2 (NCO)Cl2- complex.
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Affiliation(s)
- Aleksandra Leszczyk
- Institute of Physics, Faculty of Physics, Astronomy and Informatics, Nicolaus Copernicus University in Toruń, Grudziadzka 5, 87-100 Toruń, Poland.
| | - Tibor Dome
- Institute for Theoretical Physics, ETH Zürich, 8093 Zürich, Switzerland.,Institute of Astronomy, University of Cambridge, Madingley Road Cambridge, CB3 0HA, UK
| | - Paweł Tecmer
- Institute of Physics, Faculty of Physics, Astronomy and Informatics, Nicolaus Copernicus University in Toruń, Grudziadzka 5, 87-100 Toruń, Poland.
| | - Dariusz Kedziera
- Faculty of Chemistry, Nicolaus Copernicus University in Toruń, Gagarina 7, 87-100 Toruń, Poland
| | - Katharina Boguslawski
- Institute of Physics, Faculty of Physics, Astronomy and Informatics, Nicolaus Copernicus University in Toruń, Grudziadzka 5, 87-100 Toruń, Poland.
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12
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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: 0.5] [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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13
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Drwal D, Beran P, Hapka M, Modrzejewski M, Sokół A, Veis L, Pernal K. Efficient Adiabatic Connection Approach for Strongly Correlated Systems: Application to Singlet-Triplet Gaps of Biradicals. J Phys Chem Lett 2022; 13:4570-4578. [PMID: 35580342 PMCID: PMC9150121 DOI: 10.1021/acs.jpclett.2c00993] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Accepted: 05/09/2022] [Indexed: 06/15/2023]
Abstract
Strong electron correlation can be captured with multireference wave function methods, but an accurate description of the electronic structure requires accounting for the dynamic correlation, which they miss. In this work, a new approach for the correlation energy based on the adiabatic connection (AC) is proposed. The ACn method accounts for terms up to order n in the coupling constant, and it is size-consistent and free from instabilities. It employs the multireference random phase approximation and the Cholesky decomposition technique, leading to a computational cost growing with the fifth power of the system size. Because of the dependence on only one- and two-electron reduced density matrices, ACn is more efficient than existing ab initio multireference dynamic correlation methods. ACn affords excellent results for singlet-triplet gaps of challenging organic biradicals. The development presented in this work opens new perspectives for accurate calculations of systems with dozens of strongly correlated electrons.
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Affiliation(s)
- Daria Drwal
- Institute
of Physics, Lodz University of Technology, ul. Wolczanska 219, 90-924 Lodz, Poland
| | - Pavel Beran
- J.
Heyrovský Institute of Physical Chemistry, Academy of Sciences of the Czech Republic, v.v.i., Dolejškova 3, 18223 Prague 8, Czech Republic
- Faculty
of Mathematics and Physics, Charles University, Ke Karlovu 2027/3, 12116 Prague 2, Czech Republic
| | - Michał Hapka
- Institute
of Physics, Lodz University of Technology, ul. Wolczanska 219, 90-924 Lodz, Poland
- Faculty
of Chemistry, University of Warsaw, ul. L. Pasteura 1, 02-093 Warsaw, Poland
| | - Marcin Modrzejewski
- Faculty
of Chemistry, University of Warsaw, ul. L. Pasteura 1, 02-093 Warsaw, Poland
| | - Adam Sokół
- Institute
of Physics, Lodz University of Technology, ul. Wolczanska 219, 90-924 Lodz, Poland
| | - Libor Veis
- J.
Heyrovský Institute of Physical Chemistry, Academy of Sciences of the Czech Republic, v.v.i., Dolejškova 3, 18223 Prague 8, Czech Republic
| | - Katarzyna Pernal
- Institute
of Physics, Lodz University of Technology, ul. Wolczanska 219, 90-924 Lodz, Poland
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14
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Rask AE, Zimmerman PM. The many-body electronic interactions of Fe(II)–porphyrin. J Chem Phys 2022; 156:094110. [DOI: 10.1063/5.0079310] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
Fe(II)–porphyrin complexes exhibit a diverse range of electronic interactions between the metal and macrocycle. Herein, the incremental full configuration interaction method is applied to the entire space of valence orbitals of a Fe(II)–porphyrin model using a modest basis set. A novel visualization framework is proposed to analyze individual many-body contributions to the correlation energy, providing detailed maps of this complex’s highly correlated electronic structure. This technique is used to parse the numerous interactions of two low-lying triplet states (3A2g and 3Eg) and to show that strong metal d–d and macrocycle π–π orbital interactions preferentially stabilize the 3A2g state. d–π interactions, on the other hand, preferentially stabilize the 3Eg state and primarily appear when correlating six electrons at a time. Ultimately, the Fe(II)–porphyrin model’s full set of 88 valence electrons are correlated in 275 orbitals, showing the interactions up to the 4-body level, which covers the great majority of correlations in this system.
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Affiliation(s)
- A. E. Rask
- Department of Chemistry, University of Michigan, 930 N. University Ave., Ann Arbor, Michigan 48109, USA
| | - P. M. Zimmerman
- Department of Chemistry, University of Michigan, 930 N. University Ave., Ann Arbor, Michigan 48109, USA
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15
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Affiliation(s)
- Milica Feldt
- Leibniz Institute for Catalysis: Leibniz-Institut fur Katalyse eV Theory & Catalysis Albert-Einstein-Str 29A 18059 Rostock GERMANY
| | - Quan Manh Phung
- Nagoya University: Nagoya Daigaku Department of Chemistry JAPAN
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16
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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: 0.5] [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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