1
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Wang Y, Zhang IY, Xu X. How accurate can Kohn-Sham density functional be for both main-group and transition metal reactions. J Comput Chem 2024. [PMID: 39211974 DOI: 10.1002/jcc.27488] [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: 07/09/2024] [Revised: 08/02/2024] [Accepted: 08/08/2024] [Indexed: 09/04/2024]
Abstract
Achieving chemical accuracy in describing reactions involving both main-group elements and transition metals poses a substantial challenge for density functional approximations (DFAs), primarily due to the significantly different behaviors for electrons moving in the s,p-orbitals or in the d,f-orbitals. MOR41, a representative dataset of transition metal chemistry, has highlighted the PWPB95-D3(BJ) functional, a B2PLYP-type doubly hybrid (bDH) approximation equipped with an empirical dispersion correction, as the leading functional thus far (Dohm et al., J Chem Theory Comput 2018;14: 2596-2608). However, this functional is not among the top bDH methods for main-group chemistry (Goerigk et al., Phys Chem Chem Phys. 2017;19: 32184). Conversely, bDH methods such as DSD-BLYP-D3, proficient in main-group chemistry, often falter for transition metal chemistry. Herein, taking advantage of the home-made Rust-based Electronic-Structure Toolkits, we examine a suite of XYG3-type doubly hybrid (xDH) methods. We confirm that the trade-off in descriptive accuracy between main-group and transition metal systems persists within the realm of perturbation theory (PT2)-based xDH methods. Notably, however, our study ushers in a pivotal advance with the recently proposed renormalized xDH method, R-xDH7-SCC15. This method not only distinguishes itself among the elite methods for main-group chemistry, but also achieves an unprecedented accuracy for the MOR41 dataset, outperforming all other reported DFAs. The efficacy of R-xDH7-SCC15 stems from the successful integration of a renormalized PT2 correlation model (rPT2) and a machine-learning strong-correlation correction (SCC15), marking a significant step forward in the realm of computational chemistry.
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Affiliation(s)
- Yizhen Wang
- Shanghai Key Laboratory of Molecular Catalysis and Innovation Materials, Collaborative Innovation Centre of Chemistry for Energy Materials, MOE Laboratory for Computational Physical Science, Department of Chemistry, Fudan University, Shanghai, China
| | - Igor Ying Zhang
- Shanghai Key Laboratory of Molecular Catalysis and Innovation Materials, Collaborative Innovation Centre of Chemistry for Energy Materials, MOE Laboratory for Computational Physical Science, Department of Chemistry, Fudan University, Shanghai, China
- Hefei National Laboratory, Hefei, China
- Shanghai Key Laboratory of Bioactive Small Molecules, Shanghai, China
| | - Xin Xu
- Shanghai Key Laboratory of Molecular Catalysis and Innovation Materials, Collaborative Innovation Centre of Chemistry for Energy Materials, MOE Laboratory for Computational Physical Science, Department of Chemistry, Fudan University, Shanghai, China
- Hefei National Laboratory, Hefei, China
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2
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Wang Y, Lin Z, Ouyang R, Jiang B, Zhang IY, Xu X. Toward Efficient and Unified Treatment of Static and Dynamic Correlations in Generalized Kohn-Sham Density Functional Theory. JACS AU 2024; 4:3205-3216. [PMID: 39211596 PMCID: PMC11350721 DOI: 10.1021/jacsau.4c00488] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/10/2024] [Revised: 07/26/2024] [Accepted: 08/06/2024] [Indexed: 09/04/2024]
Abstract
Accurate description of the static correlation poses a persistent challenge in electronic structure theory, particularly when it has to be concurrently considered with the dynamic correlation. We develop here a method in the generalized Kohn-Sham density functional theory (DFT) framework, named R-xDH7-SCC15, which achieves an unprecedented accuracy in capturing the static correlation, while maintaining a good description of the dynamic correlation on par with the state-of-the-art DFT and wave function theory methods, all grounded in the same single-reference black-box methodology. Central to R-xDH7-SCC15 is a general-purpose static correlation correction (SCC) model applied to the renormalized XYG3-type doubly hybrid method (R-xDH7). The SCC model development involves a hybrid machine learning strategy that integrates symbolic regression with nonlinear parameter optimization, aiming to achieve a balance between generalization capability, numerical accuracy, and interpretability. Extensive benchmark studies confirm the robustness and broad applicability of R-xDH7-SCC15 across a diverse array of main-group chemical scenarios. Notably, it displays exceptional aptitude in accurately characterizing intricate reaction kinetics and dynamic processes in regions distant from equilibrium, where the influence of static correlation is most profound. Its capability to consistently and efficiently predict the whole energy profiles, activation barriers, and reaction pathways within a user-friendly "black-box" framework represents an important advance in the field of electronic structure theory.
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Affiliation(s)
- Yizhen Wang
- Shanghai
Key Laboratory of Molecular Catalysis and Innovation Materials, Collaborative
Innovation Centre of Chemistry for Energy Materials, MOE Laboratory
for Computational Physical Science, Department of Chemistry, Fudan University, Shanghai 200438, China
| | - Zihan Lin
- Shanghai
Key Laboratory of Molecular Catalysis and Innovation Materials, Collaborative
Innovation Centre of Chemistry for Energy Materials, MOE Laboratory
for Computational Physical Science, Department of Chemistry, Fudan University, Shanghai 200438, China
| | - Runhai Ouyang
- Materials
Genome Institute, Shanghai University, Shanghai 200444, China
| | - Bin Jiang
- Key
Laboratory of Precision and Intelligent Chemistry, Department of Chemical
Physics, University of Science and Technology
of China, Hefei, Anhui 230026, China
- Hefei
National Laboratory, Hefei 230088, China
| | - Igor Ying Zhang
- Shanghai
Key Laboratory of Molecular Catalysis and Innovation Materials, Collaborative
Innovation Centre of Chemistry for Energy Materials, MOE Laboratory
for Computational Physical Science, Department of Chemistry, Fudan University, Shanghai 200438, China
- Hefei
National Laboratory, Hefei 230088, China
- Shanghai
Key Laboratory of Bioactive Small Molecules, Shanghai200032, China
| | - Xin Xu
- Shanghai
Key Laboratory of Molecular Catalysis and Innovation Materials, Collaborative
Innovation Centre of Chemistry for Energy Materials, MOE Laboratory
for Computational Physical Science, Department of Chemistry, Fudan University, Shanghai 200438, China
- Hefei
National Laboratory, Hefei 230088, China
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3
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Gould T, Chan B, Dale SG, Vuckovic S. Identifying and embedding transferability in data-driven representations of chemical space. Chem Sci 2024; 15:11122-11133. [PMID: 39027290 PMCID: PMC11253166 DOI: 10.1039/d4sc02358g] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2024] [Accepted: 06/02/2024] [Indexed: 07/20/2024] Open
Abstract
Transferability, especially in the context of model generalization, is a paradigm of all scientific disciplines. However, the rapid advancement of machine learned model development threatens this paradigm, as it can be difficult to understand how transferability is embedded (or missed) in complex models developed using large training data sets. Two related open problems are how to identify, without relying on human intuition, what makes training data transferable; and how to embed transferability into training data. To solve both problems for ab initio chemical modelling, an indispensable tool in everyday chemistry research, we introduce a transferability assessment tool (TAT) and demonstrate it on a controllable data-driven model for developing density functional approximations (DFAs). We reveal that human intuition in the curation of training data introduces chemical biases that can hamper the transferability of data-driven DFAs. We use our TAT to motivate three transferability principles; one of which introduces the key concept of transferable diversity. Finally, we propose data curation strategies for general-purpose machine learning models in chemistry that identify and embed the transferability principles.
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Affiliation(s)
- Tim Gould
- Queensland Micro- and Nanotechnology Centre, Griffith University Nathan Qld 4111 Australia
| | - Bun Chan
- Graduate School of Engineering, Nagasaki University Bunkyo 1-14 Nagasaki 852-8521 Japan
| | - Stephen G Dale
- Queensland Micro- and Nanotechnology Centre, Griffith University Nathan Qld 4111 Australia
- Institute of Functional Intelligent Materials, National University of Singapore 4 Science Drive 2 Singapore 117544
| | - Stefan Vuckovic
- Department of Chemistry, University of Fribourg Fribourg Switzerland
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4
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Daas KJ, Kooi DP, Peters NC, Fabiano E, Della Sala F, Gori-Giorgi P, Vuckovic S. Regularized and Opposite Spin-Scaled Functionals from Møller-Plesset Adiabatic Connection─Higher Accuracy at Lower Cost. J Phys Chem Lett 2023; 14:8448-8459. [PMID: 37721318 DOI: 10.1021/acs.jpclett.3c01832] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/19/2023]
Abstract
Noncovalent interactions (NCIs) play a crucial role in biology, chemistry, material science, and everything in between. To improve pure quantum-chemical simulations of NCIs, we propose a methodology for constructing approximate correlation energies by combining an interpolation along the Møller-Plesset adiabatic connection (MP AC) with a regularization and spin-scaling strategy applied to MP2 correlation energies. This combination yields cosκos-SPL2, which exhibits superior accuracy for NCIs compared to any of the individual strategies. With the N4 formal scaling, cosκos-SPL2 is competitive or often outperforms more expensive dispersion-corrected double hybrids for NCIs. The accuracy of cosκos-SPL2 particularly shines for anionic halogen bonded complexes, where it surpasses standard dispersion-corrected DFT by a factor of 3 to 5.
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Affiliation(s)
- Kimberly J Daas
- Department of Chemistry & Pharmaceutical Sciences and Amsterdam Institute of Molecular and Life Sciences (AIMMS), Faculty of Science, Vrije Universiteit, De Boelelaan 1083, 1081HV Amsterdam, The Netherlands
| | - Derk P Kooi
- Department of Chemistry & Pharmaceutical Sciences and Amsterdam Institute of Molecular and Life Sciences (AIMMS), Faculty of Science, Vrije Universiteit, De Boelelaan 1083, 1081HV Amsterdam, The Netherlands
- Microsoft Research AI4Science, Evert van de Beekstraat 354, 1118CZ Schiphol, The Netherlands
| | - Nina C Peters
- Department of Chemistry & Pharmaceutical Sciences and Amsterdam Institute of Molecular and Life Sciences (AIMMS), Faculty of Science, Vrije Universiteit, De Boelelaan 1083, 1081HV Amsterdam, The Netherlands
| | - Eduardo Fabiano
- Institute for Microelectronics and Microsystems (CNR-IMM), Via Monteroni, Campus Unisalento, 73100 Lecce, Italy
- Center for Biomolecular Nanotechnologies, Istituto Italiano di Tecnologia, 73010 Arnesano, Italy
| | - Fabio Della Sala
- Institute for Microelectronics and Microsystems (CNR-IMM), Via Monteroni, Campus Unisalento, 73100 Lecce, Italy
- Center for Biomolecular Nanotechnologies, Istituto Italiano di Tecnologia, 73010 Arnesano, Italy
| | - Paola Gori-Giorgi
- Department of Chemistry & Pharmaceutical Sciences and Amsterdam Institute of Molecular and Life Sciences (AIMMS), Faculty of Science, Vrije Universiteit, De Boelelaan 1083, 1081HV Amsterdam, The Netherlands
- Microsoft Research AI4Science, Evert van de Beekstraat 354, 1118CZ Schiphol, The Netherlands
| | - Stefan Vuckovic
- Department of Chemistry, Faculty of Science and Medicine, Université de Fribourg/Universität Freiburg, Chemin du Musée 9, CH-1700 Fribourg, Switzerland
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5
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Hammoudan I, Chafi M. QSAR modeling of pyrazoline derivative as carbonic anhydrase inhibitors. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023:10.1007/s11356-023-28277-3. [PMID: 37405604 DOI: 10.1007/s11356-023-28277-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Received: 02/08/2023] [Accepted: 06/12/2023] [Indexed: 07/06/2023]
Abstract
The efficacy of 34 pyrazoline derivatives as carbonic anhydrase inhibitors was studied in silico. The quantum descriptors were calculated by the DFT/B3LYP method using the 6-31G(d) basis; the dataset was randomly divided into training and testing. By altering the compounds in the sets, four models were created, and they were then used to determine the predicted pIC50 values for the six chemicals in the test set. According to the OECD guidelines for QSAR model validation and the Golbraikh and Tropsha's criteria for model approval, each created model was independently validated both internally and externally, along with YRandomization. Model 3 is chosen because it has higher R2, R2test, and Q2cv values (R2 = 0.79, R2test = 0.95, Q2cv = 0.64). Only one descriptor has a proportional influence on pIC50 activity, but the other four descriptors have an inverse influence on pIC50 because of the negative contribution coefficient. Given the descriptors of the model, we could propose new molecules with remarkable inhibitory activity.
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Affiliation(s)
- Imad Hammoudan
- LIPE, Higher School of Technology, University Hassan II of Casablanca, B.P 8012 Oasis, Casablanca, Morocco
| | - Mohammed Chafi
- LIPE, Higher School of Technology, University Hassan II of Casablanca, B.P 8012 Oasis, Casablanca, Morocco.
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6
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Becke AD, Santra G, Martin JML. A double-hybrid density functional based on good local physics with outstanding performance on the GMTKN55 database. J Chem Phys 2023; 158:2882268. [PMID: 37094004 DOI: 10.1063/5.0141238] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2023] [Accepted: 03/20/2023] [Indexed: 04/26/2023] Open
Abstract
In two recent papers [A. D. Becke, J. Chem. Phys. 156, 214101 (2022) and A. D. Becke, J. Chem. Phys. 157, 234102 (2022)], we compared two Kohn-Sham density functionals based on physical modeling and theory with the best density-functional power-series fits in the literature. The best error statistics reported to date for a hybrid functional on the general main-group thermochemistry, kinetics, and noncovalent interactions (GMTKN55) chemical database of Goerigk et al. [Phys. Chem. Chem. Phys. 19, 32184 (2017)] were obtained. In the present work, additional second-order perturbation-theory terms are considered. The result is a 12-parameter double-hybrid density functional with the lowest GMTKN55 WTMAD2 "weighted total mean absolute deviation" error (1.76 kcal/mol) yet seen for any hybrid or double-hybrid density-functional approximation. We call it "DH23."
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Affiliation(s)
- Axel D Becke
- Department of Chemistry, Dalhousie University, 6274 Coburg Road, P.O. Box 15000, Halifax, Nova Scotia B3H 4R2, Canada
| | - Golokesh Santra
- Department of Molecular Chemistry and Materials Science, Weizmann Institute of Science, 7610001 Rehovot, Israel
| | - Jan M L Martin
- Department of Molecular Chemistry and Materials Science, Weizmann Institute of Science, 7610001 Rehovot, Israel
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7
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Teale AM, Helgaker T, Savin A, Adamo C, Aradi B, Arbuznikov AV, Ayers PW, Baerends EJ, Barone V, Calaminici P, Cancès E, Carter EA, Chattaraj PK, Chermette H, Ciofini I, Crawford TD, De Proft F, Dobson JF, Draxl C, Frauenheim T, Fromager E, Fuentealba P, Gagliardi L, Galli G, Gao J, Geerlings P, Gidopoulos N, Gill PMW, Gori-Giorgi P, Görling A, Gould T, Grimme S, Gritsenko O, Jensen HJA, Johnson ER, Jones RO, Kaupp M, Köster AM, Kronik L, Krylov AI, Kvaal S, Laestadius A, Levy M, Lewin M, Liu S, Loos PF, Maitra NT, Neese F, Perdew JP, Pernal K, Pernot P, Piecuch P, Rebolini E, Reining L, Romaniello P, Ruzsinszky A, Salahub DR, Scheffler M, Schwerdtfeger P, Staroverov VN, Sun J, Tellgren E, Tozer DJ, Trickey SB, Ullrich CA, Vela A, Vignale G, Wesolowski TA, Xu X, Yang W. DFT exchange: sharing perspectives on the workhorse of quantum chemistry and materials science. Phys Chem Chem Phys 2022; 24:28700-28781. [PMID: 36269074 PMCID: PMC9728646 DOI: 10.1039/d2cp02827a] [Citation(s) in RCA: 62] [Impact Index Per Article: 31.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Accepted: 08/09/2022] [Indexed: 12/13/2022]
Abstract
In this paper, the history, present status, and future of density-functional theory (DFT) is informally reviewed and discussed by 70 workers in the field, including molecular scientists, materials scientists, method developers and practitioners. The format of the paper is that of a roundtable discussion, in which the participants express and exchange views on DFT in the form of 302 individual contributions, formulated as responses to a preset list of 26 questions. Supported by a bibliography of 777 entries, the paper represents a broad snapshot of DFT, anno 2022.
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Affiliation(s)
- Andrew M. Teale
- School of Chemistry, University of Nottingham, University ParkNottinghamNG7 2RDUK
| | - Trygve Helgaker
- Hylleraas Centre for Quantum Molecular Sciences, Department of Chemistry, University of Oslo, P.O. Box 1033 Blindern, N-0315 Oslo, Norway.
| | - Andreas Savin
- Laboratoire de Chimie Théorique, CNRS and Sorbonne University, 4 Place Jussieu, CEDEX 05, 75252 Paris, France.
| | - Carlo Adamo
- PSL University, CNRS, ChimieParisTech-PSL, Institute of Chemistry for Health and Life Sciences, i-CLeHS, 11 rue P. et M. Curie, 75005 Paris, France.
| | - Bálint Aradi
- Bremen Center for Computational Materials Science, University of Bremen, P.O. Box 330440, D-28334 Bremen, Germany.
| | - Alexei V. Arbuznikov
- Technische Universität Berlin, Institut für Chemie, Theoretische Chemie/Quantenchemie, Sekr. C7Straße des 17. Juni 13510623Berlin
| | | | - Evert Jan Baerends
- Department of Chemistry and Pharmaceutical Sciences, Faculty of Science, Vrije Universiteit, De Boelelaan 1083, 1081HV Amsterdam, The Netherlands.
| | - Vincenzo Barone
- Scuola Normale Superiore, Piazza dei Cavalieri 7, 56125 Pisa, Italy.
| | - Patrizia Calaminici
- Departamento de Química, Centro de Investigación y de Estudios Avanzados (Cinvestav), CDMX, 07360, Mexico.
| | - Eric Cancès
- CERMICS, Ecole des Ponts and Inria Paris, 6 Avenue Blaise Pascal, 77455 Marne-la-Vallée, France.
| | - Emily A. Carter
- Department of Mechanical and Aerospace Engineering and the Andlinger Center for Energy and the Environment, Princeton UniversityPrincetonNJ 08544-5263USA
| | | | - Henry Chermette
- Institut Sciences Analytiques, Université Claude Bernard Lyon1, CNRS UMR 5280, 69622 Villeurbanne, France.
| | - Ilaria Ciofini
- PSL University, CNRS, ChimieParisTech-PSL, Institute of Chemistry for Health and Life Sciences, i-CLeHS, 11 rue P. et M. Curie, 75005 Paris, France.
| | - T. Daniel Crawford
- Department of Chemistry, Virginia TechBlacksburgVA 24061USA,Molecular Sciences Software InstituteBlacksburgVA 24060USA
| | - Frank De Proft
- Research Group of General Chemistry (ALGC), Vrije Universiteit Brussel (VUB), Pleinlaan 2, B-1050 Brussels, Belgium.
| | | | - Claudia Draxl
- Institut für Physik and IRIS Adlershof, Humboldt-Universität zu Berlin, 12489 Berlin, Germany. .,Fritz-Haber-Institut der Max-Planck-Gesellschaft, 14195 Berlin, Germany
| | - Thomas Frauenheim
- Bremen Center for Computational Materials Science, University of Bremen, P.O. Box 330440, D-28334 Bremen, Germany. .,Beijing Computational Science Research Center (CSRC), 100193 Beijing, China.,Shenzhen JL Computational Science and Applied Research Institute, 518110 Shenzhen, China
| | - Emmanuel Fromager
- Laboratoire de Chimie Quantique, Institut de Chimie, CNRS/Université de Strasbourg, 4 rue Blaise Pascal, 67000 Strasbourg, France.
| | - Patricio Fuentealba
- Departamento de Física, Facultad de Ciencias, Universidad de Chile, Casilla 653, Santiago, Chile.
| | - Laura Gagliardi
- Department of Chemistry, Pritzker School of Molecular Engineering, The James Franck Institute, and Chicago Center for Theoretical Chemistry, The University of Chicago, Chicago, Illinois 60637, USA.
| | - Giulia Galli
- Pritzker School of Molecular Engineering and Department of Chemistry, The University of Chicago, Chicago, IL, USA.
| | - Jiali Gao
- Institute of Systems and Physical Biology, Shenzhen Bay Laboratory, Shenzhen 518055, China. .,Department of Chemistry, University of Minnesota, Minneapolis, MN 55455, USA
| | - Paul Geerlings
- Research Group of General Chemistry (ALGC), Vrije Universiteit Brussel (VUB), Pleinlaan 2, B-1050 Brussels, Belgium.
| | - Nikitas Gidopoulos
- Department of Physics, Durham University, South Road, Durham DH1 3LE, UK.
| | - Peter M. W. Gill
- School of Chemistry, University of SydneyCamperdown NSW 2006Australia
| | - Paola Gori-Giorgi
- Department of Chemistry and Pharmaceutical Sciences, Amsterdam Institute of Molecular and Life Sciences (AIMMS), Faculty of Science, Vrije Universiteit, De Boelelaan 1083, 1081HV Amsterdam, The Netherlands.
| | - Andreas Görling
- Chair of Theoretical Chemistry, University of Erlangen-Nuremberg, Egerlandstrasse 3, 91058 Erlangen, Germany.
| | - Tim Gould
- Qld Micro- and Nanotechnology Centre, Griffith University, Gold Coast, Qld 4222, Australia.
| | - Stefan Grimme
- Mulliken Center for Theoretical Chemistry, University of Bonn, Beringstrasse 4, 53115 Bonn, Germany.
| | - Oleg Gritsenko
- Department of Chemistry and Pharmaceutical Sciences, Amsterdam Institute of Molecular and Life Sciences (AIMMS), Faculty of Science, Vrije Universiteit, De Boelelaan 1083, 1081HV Amsterdam, The Netherlands.
| | - Hans Jørgen Aagaard Jensen
- Department of Physics, Chemistry and Pharmacy, University of Southern Denmark, DK-5230 Odense M, Denmark.
| | - Erin R. Johnson
- Department of Chemistry, Dalhousie UniversityHalifaxNova ScotiaB3H 4R2Canada
| | - Robert O. Jones
- Peter Grünberg Institut PGI-1, Forschungszentrum Jülich52425 JülichGermany
| | - Martin Kaupp
- Technische Universität Berlin, Institut für Chemie, Theoretische Chemie/Quantenchemie, Sekr. C7, Straße des 17. Juni 135, 10623, Berlin.
| | - Andreas M. Köster
- Departamento de Química, Centro de Investigación y de Estudios Avanzados (Cinvestav)CDMX07360Mexico
| | - Leeor Kronik
- Department of Molecular Chemistry and Materials Science, Weizmann Institute of Science, Rehovoth, 76100, Israel.
| | - Anna I. Krylov
- Department of Chemistry, University of Southern CaliforniaLos AngelesCalifornia 90089USA
| | - Simen Kvaal
- Hylleraas Centre for Quantum Molecular Sciences, Department of Chemistry, University of Oslo, P.O. Box 1033 Blindern, N-0315 Oslo, Norway.
| | - Andre Laestadius
- Hylleraas Centre for Quantum Molecular Sciences, Department of Chemistry, University of Oslo, P.O. Box 1033 Blindern, N-0315 Oslo, Norway.
| | - Mel Levy
- Department of Chemistry, Tulane University, New Orleans, Louisiana, 70118, USA.
| | - Mathieu Lewin
- CNRS & CEREMADE, Université Paris-Dauphine, PSL Research University, Place de Lattre de Tassigny, 75016 Paris, France.
| | - Shubin Liu
- Research Computing Center, University of North Carolina, Chapel Hill, NC 27599-3420, USA. .,Department of Chemistry, University of North Carolina, Chapel Hill, NC 27599-3290, USA
| | - Pierre-François Loos
- Laboratoire de Chimie et Physique Quantiques (UMR 5626), Université de Toulouse, CNRS, UPS, France.
| | - Neepa T. Maitra
- Department of Physics, Rutgers University at Newark101 Warren StreetNewarkNJ 07102USA
| | - Frank Neese
- Max Planck Institut für Kohlenforschung, Kaiser Wilhelm Platz 1, D-45470 Mülheim an der Ruhr, Germany.
| | - John P. Perdew
- Departments of Physics and Chemistry, Temple UniversityPhiladelphiaPA 19122USA
| | - Katarzyna Pernal
- Institute of Physics, Lodz University of Technology, ul. Wolczanska 219, 90-924 Lodz, Poland.
| | - Pascal Pernot
- Institut de Chimie Physique, UMR8000, CNRS and Université Paris-Saclay, Bât. 349, Campus d'Orsay, 91405 Orsay, France.
| | - Piotr Piecuch
- Department of Chemistry, Michigan State University, East Lansing, Michigan 48824, USA. .,Department of Physics and Astronomy, Michigan State University, East Lansing, Michigan 48824, USA
| | - Elisa Rebolini
- Institut Laue Langevin, 71 avenue des Martyrs, 38000 Grenoble, France.
| | - Lucia Reining
- Laboratoire des Solides Irradiés, CNRS, CEA/DRF/IRAMIS, École Polytechnique, Institut Polytechnique de Paris, F-91120 Palaiseau, France. .,European Theoretical Spectroscopy Facility
| | - Pina Romaniello
- Laboratoire de Physique Théorique (UMR 5152), Université de Toulouse, CNRS, UPS, France.
| | - Adrienn Ruzsinszky
- Department of Physics, Temple University, Philadelphia, Pennsylvania 19122, USA.
| | - Dennis R. Salahub
- Department of Chemistry, Department of Physics and Astronomy, CMS – Centre for Molecular Simulation, IQST – Institute for Quantum Science and Technology, Quantum Alberta, University of Calgary2500 University Drive NWCalgaryAlbertaT2N 1N4Canada
| | - Matthias Scheffler
- The NOMAD Laboratory at the FHI of the Max-Planck-Gesellschaft and IRIS-Adlershof of the Humboldt-Universität zu Berlin, Faradayweg 4-6, D-14195, Germany.
| | - Peter Schwerdtfeger
- Centre for Theoretical Chemistry and Physics, The New Zealand Institute for Advanced Study, Massey University Auckland, 0632 Auckland, New Zealand.
| | - Viktor N. Staroverov
- Department of Chemistry, The University of Western OntarioLondonOntario N6A 5B7Canada
| | - Jianwei Sun
- Department of Physics and Engineering Physics, Tulane University, New Orleans, LA 70118, USA.
| | - Erik Tellgren
- Hylleraas Centre for Quantum Molecular Sciences, Department of Chemistry, University of Oslo, P.O. Box 1033 Blindern, N-0315 Oslo, Norway.
| | - David J. Tozer
- Department of Chemistry, Durham UniversitySouth RoadDurhamDH1 3LEUK
| | - Samuel B. Trickey
- Quantum Theory Project, Deptartment of Physics, University of FloridaGainesvilleFL 32611USA
| | - Carsten A. Ullrich
- Department of Physics and Astronomy, University of MissouriColumbiaMO 65211USA
| | - Alberto Vela
- Departamento de Química, Centro de Investigación y de Estudios Avanzados (Cinvestav), CDMX, 07360, Mexico.
| | - Giovanni Vignale
- Department of Physics, University of Missouri, Columbia, MO 65203, USA.
| | - Tomasz A. Wesolowski
- Department of Physical Chemistry, Université de Genève30 Quai Ernest-Ansermet1211 GenèveSwitzerland
| | - Xin Xu
- Shanghai Key Laboratory of Molecular Catalysis and Innovation Materials, Collaborative Innovation Centre of Chemistry for Energy Materials, MOE Laboratory for Computational Physical Science, Department of Chemistry, Fudan University, Shanghai 200433, China.
| | - Weitao Yang
- Department of Chemistry and Physics, Duke University, Durham, NC 27516, USA.
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8
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Tan S, Wang Y, Zhang IY, Xu X. Assessment of advanced xDH@B3LYP methods in describing various potential energy curves driven by π- π, CH/ π, and SH/ π non-bonded interactions. CHINESE J CHEM PHYS 2022. [DOI: 10.1063/1674-0068/cjcp2206098] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Accurate description of potential energy curves driven by non-bonded interactions remains a great challenge for pure density functional approximations (DFAs). It is because the R−6 decay behavior of dispersion cannot be intrinsically captured by the (semi)-local ingredients and the exact-exchange used in the popular hybrid DFAs. Overemphasizing the accuracy on the equilibrium region for the functional construction would likely deteriorate the overall performance on the other regions of potential energy surfaces. In consequence, the empirical dispersion correction becomes the standard component in DFAs to treat the non-bonded interactions. In this Letter, we demonstrate that without the use of empirical dispersion correction, doubly hybrid approximations, in particular two recently proposed revXYG3 and XYG7 functionals, hold the promise to have a balanced description of non-bonded interactions on the whole potential energy curves for several prototypes of π- π, CH/ π, and SH/ π interactions. The error of revXYG3 and XYG7 for non-bonded interactions is around 0.1 kcal/mol, and their potential energy curves almost coincide with the accurate CCSD(T)/CBS curves.
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Affiliation(s)
- Shiqian Tan
- Collaborative Innovation Center of Chemistry for Energy Materials, Shanghai Key Laboratory of Molecular Catalysis and Innovative Materials, MOE Key Laboratory of Computational Physical Sciences, Shanghai Key Laboratory of Bioactive Small Molecules, Department of Chemistry, Fudan University, Shanghai 200433, China
| | - Yizhen Wang
- Collaborative Innovation Center of Chemistry for Energy Materials, Shanghai Key Laboratory of Molecular Catalysis and Innovative Materials, MOE Key Laboratory of Computational Physical Sciences, Shanghai Key Laboratory of Bioactive Small Molecules, Department of Chemistry, Fudan University, Shanghai 200433, China
| | - Igor Ying Zhang
- Collaborative Innovation Center of Chemistry for Energy Materials, Shanghai Key Laboratory of Molecular Catalysis and Innovative Materials, MOE Key Laboratory of Computational Physical Sciences, Shanghai Key Laboratory of Bioactive Small Molecules, Department of Chemistry, Fudan University, Shanghai 200433, China
| | - Xin Xu
- Collaborative Innovation Center of Chemistry for Energy Materials, Shanghai Key Laboratory of Molecular Catalysis and Innovative Materials, MOE Key Laboratory of Computational Physical Sciences, Shanghai Key Laboratory of Bioactive Small Molecules, Department of Chemistry, Fudan University, Shanghai 200433, China
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9
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Yan W, Xu X. Analytic Gradients for the Long-Range-Corrected XYG3 Type of Doubly Hybrid Density Functionals: Theory, Implementation, and Assessment. J Phys Chem A 2022; 126:3937-3946. [PMID: 35686854 DOI: 10.1021/acs.jpca.2c01962] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
An analytic gradient algorithm for the long-range-corrected (lrc-) XYG3 type of doubly hybrid functionals has been derived and implemented. Due to the introduction of a long-range second-order perturbation (lrPT2) as the correction, the Z-vector equations need to be modified, where the construction of a unique total Lagrangian of the method is required. Geometry optimizations using lrc-XYG3 and lrc-XYGJ-OS have then been applied to the A21 data set that consists of 21 noncovalently interacting systems with CCSD(T) reference data for structures, in order to testify the idea of introducing lrPT2 for a better description of intermolecular interactions. While lrc-XYG3 was observed to offer a better description in dispersion-dominant systems as compared to the XYG3 functional, lrc-XYGJ-OS stands out with an overall better balanced performance in reproducing intermolecular geometries.
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Affiliation(s)
- Wenjie Yan
- Collaborative Innovation Center of Chemistry for Energy Materials, Shanghai Key Laboratory of Molecular Catalysis and Innovative Materials, Ministry of Education Key Laboratory of Computational Physical Sciences, Department of Chemistry, Fudan University, Shanghai, 200433, China
| | - Xin Xu
- Collaborative Innovation Center of Chemistry for Energy Materials, Shanghai Key Laboratory of Molecular Catalysis and Innovative Materials, Ministry of Education Key Laboratory of Computational Physical Sciences, Department of Chemistry, Fudan University, Shanghai, 200433, China
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10
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Yan W, Xu X. Accurate Prediction of Nuclear Magnetic Resonance Parameters via the XYG3 Type of Doubly Hybrid Density Functionals. J Chem Theory Comput 2022; 18:2931-2946. [PMID: 35467852 DOI: 10.1021/acs.jctc.2c00055] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Nuclear magnetic resonance (NMR) spectroscopy is one of the most powerful and versatile tools in elucidating molecular structures. To eliminate ambiguities of experimental assignments, accurate calculations of NMR spectra are of great importance. Here, a method for theoretical evaluation of the NMR shielding constants by analytic derivatives using gauge including atomic orbitals (GIAO) has been implemented for the XYG3 type of doubly hybrid density functionals (xDH), namely, the GIAO-xDH method. Benchmark calculations on shielding constants and chemical shifts demonstrate the remarkable accuracy of the GIAO-xDH method, compared to the accurate CCSD(T) references. It is shown here that the XYGJ-OS functional is able to give a mean absolute deviation (MAD) of ∼3.0 ppm in the calculated shielding constants for 13C, 15N, 17O, 19F, while both XYGJ-OS and xDH-PBE0 functionals are able to provide a satisfactory estimation of chemical shifts with MADs of ∼0.03 and 1.0 ppm for 1H and 13C, respectively. The basis set influence upon the method has been examined and a computational scheme considering both accuracy and efficiency has been proposed and tested to predict the experimental 13C chemical shifts of five medium-sized natural product molecules, yielding a MAD of ∼1.0 ppm, which demonstrates the practical feasibility of the GIAO-xDH method.
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Affiliation(s)
- Wenjie Yan
- Collaborative Innovation Center of Chemistry for Energy Materials, Shanghai Key Laboratory of Molecular Catalysis and Innovative Materials, Ministry of Education Key Laboratory of Computational Physical Sciences, Department of Chemistry, Fudan University, Shanghai, 200433, China
| | - Xin Xu
- Collaborative Innovation Center of Chemistry for Energy Materials, Shanghai Key Laboratory of Molecular Catalysis and Innovative Materials, Ministry of Education Key Laboratory of Computational Physical Sciences, Department of Chemistry, Fudan University, Shanghai, 200433, China
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11
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Toward accurate and efficient dynamic computational strategy for heterogeneous catalysis: Temperature-dependent thermodynamics and kinetics for the chemisorbed on-surface CO. CHINESE CHEM LETT 2022. [DOI: 10.1016/j.cclet.2022.03.080] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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12
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Theoretical Description of Water from Single-Molecule to Condensed Phase: a Review of Recent Progress on Potential Energy Surfaces and Molecular Dynamics. CHINESE J CHEM PHYS 2022. [DOI: 10.1063/1674-0068/cjcp2201005] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
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13
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Li W, Wang D, Yang Z, Zhang H, Hu L, Chen G. DeepNCI: DFT Noncovalent Interaction Correction with Transferable Multimodal Three-Dimensional Convolutional Neural Networks. J Chem Inf Model 2021; 62:5090-5099. [PMID: 34958566 DOI: 10.1021/acs.jcim.1c01305] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
A multimodal deep learning model, DeepNCI, is proposed for improving noncovalent interactions (NCIs) calculated via density functional theory (DFT). DeepNCI is composed of a three-dimensional convolutional neural network (3D CNN) for abstracting critical and comprehensive features from 3D electron density, and a neural network for modeling one-dimensional quantum chemical properties. By merging features from two networks, DeepNCI is able to reduce the root-mean-square error of DFT-calculated NCI from 1.19 kcal/mol to ∼0.2 kcal/mol for a NCI molecular database (>1000 molecules). The representativeness of the joint features can be visualized by t-distributed stochastic neighbor embedding (t-SNE), where they can distinguish categorized NCI systems quite well. Therefore, the fused model performs better than its component networks. In addition, the 3D CNN takes electron density as inputs that are in the same range, despite the size of molecular systems, so it can promote model applicability and transferability. To clarify the applicability of DeepNCI, an application domain (AD) has been defined with merged features using the K-nearest-neighbor method. The calculations for external test sets are shown that AD can properly monitor the reliability for a prediction. The model transferability is tested with a small database of homolysis bond dissociation energy including only dozens of samples. With NCI database pretrained parameters, the same or better performance than the reported results is achieved by transfer learning. This suggests that the DeepNCI model is transferable and it may transfer to other relative tasks, which possibly can resolve some small sampling problems. The source code of DeepNCI can be freely accessed at https://github.com/wenzelee/DeepNCI.
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Affiliation(s)
- Wenze Li
- School of Information Science and Technology, Northeast Normal University, Changchun, 130117, China
| | - Donghan Wang
- School of Information Science and Technology, Northeast Normal University, Changchun, 130117, China
| | - Zirui Yang
- School of Information Science and Technology, Northeast Normal University, Changchun, 130117, China
| | - Huijie Zhang
- School of Information Science and Technology, Northeast Normal University, Changchun, 130117, China
| | - LiHong Hu
- School of Information Science and Technology, Northeast Normal University, Changchun, 130117, China
| | - GuanHua Chen
- Department of Chemistry, The University of Hong Kong, Hong Kong S.A.R., China
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14
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Santra G, Semidalas E, Martin JML. Surprisingly Good Performance of XYG3 Family Functionals Using a Scaled KS-MP3 Correlation. J Phys Chem Lett 2021; 12:9368-9376. [PMID: 34550706 DOI: 10.1021/acs.jpclett.1c02838] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/16/2023]
Abstract
By adding a GLPT3 (third-order Görling-Levy perturbation theory, or KS-MP3) term E3 to the XYG7 form for a double hybrid, we are able to bring down WTMAD2 (weighted total mean absolute deviation) for the very large and chemically diverse GMTKN55 benchmark to an unprecedented 1.17 kcal/mol, competitive with much costlier composite wave function ab initio approaches. Intriguingly, (a) the introduction of E3 makes an empirical dispersion correction redundant; (b) generalized gradient approximation (GGA) or meta-GGA semilocal correlation functionals offer no advantage over the local density approximation (LDA) in this framework; (c) if a dispersion correction is retained, then simple Slater exchange leads to no significant loss in accuracy. It is possible to create a six-parameter functional with WTMAD2 = 1.42 that has no post-LDA density functional theory components and no dispersion correction in the final energy.
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Affiliation(s)
- Golokesh Santra
- Department of Molecular Chemistry and Materials Science, Weizmann Institute of Science, 7610001 Reḥovot, Israel
| | - Emmanouil Semidalas
- Department of Molecular Chemistry and Materials Science, Weizmann Institute of Science, 7610001 Reḥovot, Israel
| | - Jan M L Martin
- Department of Molecular Chemistry and Materials Science, Weizmann Institute of Science, 7610001 Reḥovot, Israel
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15
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Assessment of DFT methods for the prediction of detachment energies and electronic structures of complex and multiply charged anions. COMPUT THEOR CHEM 2021. [DOI: 10.1016/j.comptc.2021.113295] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
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16
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Daas T, Fabiano E, Della Sala F, Gori-Giorgi P, Vuckovic S. Noncovalent Interactions from Models for the Møller-Plesset Adiabatic Connection. J Phys Chem Lett 2021; 12:4867-4875. [PMID: 34003655 PMCID: PMC8280728 DOI: 10.1021/acs.jpclett.1c01157] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2021] [Accepted: 05/13/2021] [Indexed: 05/08/2023]
Abstract
Given the omnipresence of noncovalent interactions (NCIs), their accurate simulations are of crucial importance across various scientific disciplines. Here we construct accurate models for the description of NCIs by an interpolation along the Møller-Plesset adiabatic connection (MP AC). Our interpolation approximates the correlation energy, by recovering MP2 at small coupling strengths and the correct large-coupling strength expansion of the MP AC, recently shown to be a functional of the Hartree-Fock density. Our models are size consistent for fragments with nondegenerate ground states, have the same cost as double hybrids, and require no dispersion corrections to capture NCIs accurately. These interpolations greatly reduce large MP2 errors for typical π-stacking complexes (e.g., benzene-pyridine dimers) and for the L7 data set. They are also competitive with state-of-the-art dispersion enhanced functionals and can even significantly outperform them for a variety of data sets, such as CT7 and L7.
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Affiliation(s)
- Timothy
J. Daas
- Department
of Chemistry & Pharmaceutical Sciences and Amsterdam Institute
of Molecular and Life Sciences (AIMMS), Faculty of Science, Vrije Universiteit, De Boelelaan 1083, 1081HV Amsterdam, The Netherlands
| | - Eduardo Fabiano
- Institute
for Microelectronics and Microsystems (CNR-IMM), Via Monteroni, Campus Unisalento, 73100 Lecce, Italy
- Center
for Biomolecular Nanotechnologies, Istituto
Italiano di Tecnologia, Via Barsanti 14, 73010 Arnesano (LE), Italy
| | - Fabio Della Sala
- Institute
for Microelectronics and Microsystems (CNR-IMM), Via Monteroni, Campus Unisalento, 73100 Lecce, Italy
- Center
for Biomolecular Nanotechnologies, Istituto
Italiano di Tecnologia, Via Barsanti 14, 73010 Arnesano (LE), Italy
| | - Paola Gori-Giorgi
- Department
of Chemistry & Pharmaceutical Sciences and Amsterdam Institute
of Molecular and Life Sciences (AIMMS), Faculty of Science, Vrije Universiteit, De Boelelaan 1083, 1081HV Amsterdam, The Netherlands
| | - Stefan Vuckovic
- Department
of Chemistry & Pharmaceutical Sciences and Amsterdam Institute
of Molecular and Life Sciences (AIMMS), Faculty of Science, Vrije Universiteit, De Boelelaan 1083, 1081HV Amsterdam, The Netherlands
- Physical
and Theoretical Chemistry, University of
Saarland, 66123 Saarbrücken, Germany
- Department
of Chemistry, University of California, Irvine, California 92697, United States
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17
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Wang Y, Li Y, Chen J, Zhang IY, Xu X. Doubly Hybrid Functionals Close to Chemical Accuracy for Both Finite and Extended Systems: Implementation and Test of XYG3 and XYGJ-OS. JACS AU 2021; 1:543-549. [PMID: 34467317 PMCID: PMC8395692 DOI: 10.1021/jacsau.1c00011] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/28/2023]
Abstract
While being widely used to understand the chemical reactions in heterogeneous catalysis or other multidisciplinary systems, a great challenge that semilocal and hybrid density functional approximations (DFAs) are facing is to deliver a uniformly accurate description for both finite and extended systems. Herein, we perform reliable and well-converged periodic calculations of two doubly hybrid approximations (DHAs), XYG3 and XYGJ-OS, and demonstrate that the good accuracy of DHAs achieved for molecules is transferable to the semiconductors and insulators. Such an accuracy is not only for energetic properties but also for the first- and second-order response properties, which is general for different kinds of chemical environments, including simple cubic bulks, perovskite-type transition metal oxides like TiO2, and heterogeneous systems like CO adsorption on the NaCl(100) surface. The present finding has strengthened the predictive power of DFT, which not only will inspire the future development of the top-rung DFAs but also will boost their applications in multidisciplinary studies with high accuracy and efficiency.
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Affiliation(s)
- Yizhen Wang
- Collaborative
Innovation Center of Chemistry for Energy Materials, Shanghai, Key
Laboratory of Molecular Catalysis and Innovative Materials, MOE Key
Laboratory of Computational Physical Sciences, Shanghai Key Laboratory
of Bioactive Small Molecules, Department of Chemistry, Fudan University, Shanghai 200433, People’s Republic of China
| | - Yajing Li
- Collaborative
Innovation Center of Chemistry for Energy Materials, Shanghai, Key
Laboratory of Molecular Catalysis and Innovative Materials, MOE Key
Laboratory of Computational Physical Sciences, Shanghai Key Laboratory
of Bioactive Small Molecules, Department of Chemistry, Fudan University, Shanghai 200433, People’s Republic of China
| | - Jun Chen
- State
Key Laboratory of Structural Chemistry, Fujian Institute of Research on the Structure of Matter, Chinese
Academy of Sciences, Fuzhou 350002, People’s Republic
of China
| | - Igor Ying Zhang
- Collaborative
Innovation Center of Chemistry for Energy Materials, Shanghai, Key
Laboratory of Molecular Catalysis and Innovative Materials, MOE Key
Laboratory of Computational Physical Sciences, Shanghai Key Laboratory
of Bioactive Small Molecules, Department of Chemistry, Fudan University, Shanghai 200433, People’s Republic of China
| | - Xin Xu
- Collaborative
Innovation Center of Chemistry for Energy Materials, Shanghai, Key
Laboratory of Molecular Catalysis and Innovative Materials, MOE Key
Laboratory of Computational Physical Sciences, Shanghai Key Laboratory
of Bioactive Small Molecules, Department of Chemistry, Fudan University, Shanghai 200433, People’s Republic of China
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