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Evangelista FA, Li C, Verma P, Hannon KP, Schriber JB, Zhang T, Cai C, Wang S, He N, Stair NH, Huang M, Huang R, Misiewicz JP, Li S, Marin K, Zhao Z, Burns LA. Forte: A suite of advanced multireference quantum chemistry methods. J Chem Phys 2024; 161:062502. [PMID: 39132791 DOI: 10.1063/5.0216512] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2024] [Accepted: 06/24/2024] [Indexed: 08/13/2024] Open
Abstract
Forte is an open-source library specialized in multireference electronic structure theories for molecular systems and the rapid prototyping of new methods. This paper gives an overview of the capabilities of Forte, its software architecture, and examples of applications enabled by the methods it implements.
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Affiliation(s)
- Francesco A Evangelista
- Department of Chemistry and Cherry Emerson Center for Scientific Computation, Emory University, Atlanta, Georgia 30322, USA
| | - Chenyang Li
- Key Laboratory of Theoretical and Computational Photochemistry, Ministry of Education, College of Chemistry, Beijing Normal University, Beijing 100875, China
| | - Prakash Verma
- Department of Chemistry and Cherry Emerson Center for Scientific Computation, Emory University, Atlanta, Georgia 30322, USA
| | - Kevin P Hannon
- Department of Chemistry and Cherry Emerson Center for Scientific Computation, Emory University, Atlanta, Georgia 30322, USA
| | - Jeffrey B Schriber
- Department of Chemistry and Cherry Emerson Center for Scientific Computation, Emory University, Atlanta, Georgia 30322, USA
- Department of Chemistry and Biochemistry, Iona University, New Rochelle, New York 10801, USA
| | - Tianyuan Zhang
- Department of Chemistry and Cherry Emerson Center for Scientific Computation, Emory University, Atlanta, Georgia 30322, USA
| | - Chenxi Cai
- Department of Chemistry and Cherry Emerson Center for Scientific Computation, Emory University, Atlanta, Georgia 30322, USA
| | - Shuhe Wang
- Department of Chemistry and Cherry Emerson Center for Scientific Computation, Emory University, Atlanta, Georgia 30322, USA
| | - Nan He
- Department of Chemistry and Cherry Emerson Center for Scientific Computation, Emory University, Atlanta, Georgia 30322, USA
| | - Nicholas H Stair
- Department of Chemistry and Cherry Emerson Center for Scientific Computation, Emory University, Atlanta, Georgia 30322, USA
| | - Meng Huang
- Department of Chemistry and Cherry Emerson Center for Scientific Computation, Emory University, Atlanta, Georgia 30322, USA
| | - Renke Huang
- Department of Chemistry and Cherry Emerson Center for Scientific Computation, Emory University, Atlanta, Georgia 30322, USA
| | - Jonathon P Misiewicz
- Department of Chemistry and Cherry Emerson Center for Scientific Computation, Emory University, Atlanta, Georgia 30322, USA
| | - Shuhang Li
- Department of Chemistry and Cherry Emerson Center for Scientific Computation, Emory University, Atlanta, Georgia 30322, USA
| | - Kevin Marin
- Department of Chemistry and Cherry Emerson Center for Scientific Computation, Emory University, Atlanta, Georgia 30322, USA
| | - Zijun Zhao
- Department of Chemistry and Cherry Emerson Center for Scientific Computation, Emory University, Atlanta, Georgia 30322, USA
| | - Lori A Burns
- Center for Computational Molecular Science and Technology, School of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, Georgia 30332-0400, USA
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2
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Pederson JP, McDaniel JG. PyDFT-QMMM: A modular, extensible software framework for DFT-based QM/MM molecular dynamics. J Chem Phys 2024; 161:034103. [PMID: 39007371 DOI: 10.1063/5.0219851] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2024] [Accepted: 06/24/2024] [Indexed: 07/16/2024] Open
Abstract
PyDFT-QMMM is a Python-based package for performing hybrid quantum mechanics/molecular mechanics (QM/MM) simulations at the density functional level of theory. The program is designed to treat short-range and long-range interactions through user-specified combinations of electrostatic and mechanical embedding procedures within periodic simulation domains, providing necessary interfaces to external quantum chemistry and molecular dynamics software. To enable direct embedding of long-range electrostatics in periodic systems, we have derived and implemented force terms for our previously described QM/MM/PME approach [Pederson and McDaniel, J. Chem. Phys. 156, 174105 (2022)]. Communication with external software packages Psi4 and OpenMM is facilitated through Python application programming interfaces (APIs). The core library contains basic utilities for running QM/MM molecular dynamics simulations, and plug-in entry-points are provided for users to implement custom energy/force calculation and integration routines, within an extensible architecture. The user interacts with PyDFT-QMMM primarily through its Python API, allowing for complex workflow development with Python scripting, for example, interfacing with PLUMED for free energy simulations. We provide benchmarks of forces and energy conservation for the QM/MM/PME and alternative QM/MM electrostatic embedding approaches. We further demonstrate a simple example use case for water solute in a water solvent system, for which radial distribution functions are computed from 100 ps QM/MM simulations; in this example, we highlight how the solvation structure is sensitive to different basis-set choices due to under- or over-polarization of the QM water molecule's electron density.
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Affiliation(s)
- John P Pederson
- School of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, Georgia 30332-0400, USA
| | - Jesse G McDaniel
- School of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, Georgia 30332-0400, USA
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3
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Zhang Y, Chen F, Liu Z, Ju Y, Cui D, Zhu J, Jiang X, Guo X, He J, Zhang L, Zhang X, Su Y. A materials terminology knowledge graph automatically constructed from text corpus. Sci Data 2024; 11:600. [PMID: 38849436 PMCID: PMC11161478 DOI: 10.1038/s41597-024-03448-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Accepted: 05/31/2024] [Indexed: 06/09/2024] Open
Abstract
A scalable, reusable, and broad-coverage unified material knowledge representation shows its importance and will bring great benefits to data sharing among materials communities. A knowledge graph (KG) for materials terminology, which is a formal collection of term entities and relationships, is conceptually important to achieve this goal. In this work, we propose a KG for materials terminology, named Materials Genome Engineering Database Knowledge Graph (MGED-KG), which is automatically constructed from text corpus via natural language processing. MGED-KG is the most comprehensive KG for materials terminology in both Chinese and English languages, consisting of 8,660 terms and their explanations. It encompasses 11 principal categories, such as Metals, Composites, Nanomaterials, each with two or three levels of subcategories, resulting in a total of 235 distinct category labels. For further application, a knowledge web system based on MGED-KG is developed and shows its great power in improving data sharing efficiency from the aspects of query expansion, term, and data recommendation.
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Affiliation(s)
- Yuwei Zhang
- School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, 100083, China
| | - Fangyi Chen
- School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, 100083, China
| | - Zeyi Liu
- School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, 100083, China
| | - Yunzhuo Ju
- School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, 100083, China
| | - Dongliang Cui
- School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, 100083, China
| | - Jinyi Zhu
- School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, 100083, China
| | - Xue Jiang
- Beijing Advanced Innovation Center for Materials Genome Engineering, Institute for Advanced Materials and Technology, University of Science and Technology Beijing, Beijing, 100083, China.
- Liaoning Academy of Materials, Shenyang, 110000, Liaoning, China.
- Shunde Innovation School, University of Science and Technology Beijing, Guangdong, 528399, China.
| | - Xi Guo
- School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, 100083, China.
- Beijing Key Laboratory of Knowledge Engineering for Materials, Beijing, 100083, China.
| | - Jie He
- School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, 100083, China.
- Liaoning Academy of Materials, Shenyang, 110000, Liaoning, China.
| | - Lei Zhang
- Beijing Advanced Innovation Center for Materials Genome Engineering, Institute for Advanced Materials and Technology, University of Science and Technology Beijing, Beijing, 100083, China
| | - Xiaotong Zhang
- School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, 100083, China
| | - Yanjing Su
- Beijing Advanced Innovation Center for Materials Genome Engineering, Institute for Advanced Materials and Technology, University of Science and Technology Beijing, Beijing, 100083, China
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4
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Crandall Z, Windus TL, Richard RM. CMaize: Simplifying inter-package modularity from the build up. J Chem Phys 2024; 160:092502. [PMID: 38445730 DOI: 10.1063/5.0196384] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2024] [Accepted: 02/18/2024] [Indexed: 03/07/2024] Open
Abstract
There is a growing desire for inter-package modularity within the chemistry software community to reuse encapsulated code units across a variety of software packages. Most comprehensive efforts at achieving inter-package modularity will quickly run afoul of a very practical problem, being able to cohesively build the modules. Writing and maintaining build systems has long been an issue for many scientific software packages that rely on compiled languages such as C/C++. The push for inter-package modularity compounds this issue by additionally requiring binary artifacts from disparate developers to interoperate at a binary level. Thankfully, the de facto build tool for C/C++, CMake, is more than capable of supporting the myriad of edge cases that complicate writing robust build systems. Unfortunately, writing and maintaining a robust CMake build system can be a laborious endeavor because CMake provides few abstractions to aid the developer. The need to significantly simplify the process of writing robust CMake-based build systems, especially in inter-package builds, motivated us to write CMaize. In addition to describing the architecture and design of CMaize, the article also demonstrates how CMaize is used in production-level software.
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Affiliation(s)
- Zachery Crandall
- Chemical and Biological Sciences, Ames National Laboratory, Ames, Iowa 50011, USA
- Department of Chemistry, Iowa State University, Ames, Iowa 50011, USA
| | - Theresa L Windus
- Chemical and Biological Sciences, Ames National Laboratory, Ames, Iowa 50011, USA
- Department of Chemistry, Iowa State University, Ames, Iowa 50011, USA
| | - Ryan M Richard
- Chemical and Biological Sciences, Ames National Laboratory, Ames, Iowa 50011, USA
- Department of Chemistry, Iowa State University, Ames, Iowa 50011, USA
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5
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Lehtola S. A call to arms: Making the case for more reusable libraries. J Chem Phys 2023; 159:180901. [PMID: 37947507 DOI: 10.1063/5.0175165] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Accepted: 10/23/2023] [Indexed: 11/12/2023] Open
Abstract
The traditional foundation of science lies on the cornerstones of theory and experiment. Theory is used to explain experiment, which in turn guides the development of theory. Since the advent of computers and the development of computational algorithms, computation has risen as the third cornerstone of science, joining theory and experiment on an equal footing. Computation has become an essential part of modern science, amending experiment by enabling accurate comparison of complicated theories to sophisticated experiments, as well as guiding by triage both the design and targets of experiments and the development of novel theories and computational methods. Like experiment, computation relies on continued investment in infrastructure: it requires both hardware (the physical computer on which the calculation is run) as well as software (the source code of the programs that performs the wanted simulations). In this Perspective, I discuss present-day challenges on the software side in computational chemistry, which arise from the fast-paced development of algorithms, programming models, as well as hardware. I argue that many of these challenges could be solved with reusable open source libraries, which are a public good, enhance the reproducibility of science, and accelerate the development and availability of state-of-the-art methods and improved software.
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Affiliation(s)
- Susi Lehtola
- Department of Chemistry, University of Helsinki, P.O. Box 55, FI-00014 Helsinki, Finland
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6
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Hermann J, Stöhr M, Góger S, Chaudhuri S, Aradi B, Maurer RJ, Tkatchenko A. libMBD: A general-purpose package for scalable quantum many-body dispersion calculations. J Chem Phys 2023; 159:174802. [PMID: 37933783 DOI: 10.1063/5.0170972] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2023] [Accepted: 10/17/2023] [Indexed: 11/08/2023] Open
Abstract
Many-body dispersion (MBD) is a powerful framework to treat van der Waals (vdW) dispersion interactions in density-functional theory and related atomistic modeling methods. Several independent implementations of MBD with varying degree of functionality exist across a number of electronic structure codes, which both limits the current users of those codes and complicates dissemination of new variants of MBD. Here, we develop and document libMBD, a library implementation of MBD that is functionally complete, efficient, easy to integrate with any electronic structure code, and already integrated in FHI-aims, DFTB+, VASP, Q-Chem, CASTEP, and Quantum ESPRESSO. libMBD is written in modern Fortran with bindings to C and Python, uses MPI/ScaLAPACK for parallelization, and implements MBD for both finite and periodic systems, with analytical gradients with respect to all input parameters. The computational cost has asymptotic cubic scaling with system size, and evaluation of gradients only changes the prefactor of the scaling law, with libMBD exhibiting strong scaling up to 256 processor cores. Other MBD properties beyond energy and gradients can be calculated with libMBD, such as the charge-density polarization, first-order Coulomb correction, the dielectric function, or the order-by-order expansion of the energy in the dipole interaction. Calculations on supramolecular complexes with MBD-corrected electronic structure methods and a meta-review of previous applications of MBD demonstrate the broad applicability of the libMBD package to treat vdW interactions.
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Affiliation(s)
- Jan Hermann
- Department of Mathematics and Computer Science, FU Berlin, 14195 Berlin, Germany
| | - Martin Stöhr
- Department of Physics and Materials Science, University of Luxembourg, L-1511 Luxembourg City, Luxembourg
| | - Szabolcs Góger
- Department of Physics and Materials Science, University of Luxembourg, L-1511 Luxembourg City, Luxembourg
| | - Shayantan Chaudhuri
- Department of Chemistry, University of Warwick, Coventry CV4 7AL, United Kingdom
| | - Bálint Aradi
- Bremen Center for Computational Materials Science, University of Bremen, 28359 Bremen, Germany
| | - Reinhard J Maurer
- Department of Chemistry, University of Warwick, Coventry CV4 7AL, United Kingdom
- Department of Physics, University of Warwick, Coventry CV4 7AL, United Kingdom
| | - Alexandre Tkatchenko
- Department of Physics and Materials Science, University of Luxembourg, L-1511 Luxembourg City, Luxembourg
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7
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Di Felice R, Mayes ML, Richard RM, Williams-Young DB, Chan GKL, de Jong WA, Govind N, Head-Gordon M, Hermes MR, Kowalski K, Li X, Lischka H, Mueller KT, Mutlu E, Niklasson AMN, Pederson MR, Peng B, Shepard R, Valeev EF, van Schilfgaarde M, Vlaisavljevich B, Windus TL, Xantheas SS, Zhang X, Zimmerman PM. A Perspective on Sustainable Computational Chemistry Software Development and Integration. J Chem Theory Comput 2023; 19:7056-7076. [PMID: 37769271 PMCID: PMC10601486 DOI: 10.1021/acs.jctc.3c00419] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2023] [Indexed: 09/30/2023]
Abstract
The power of quantum chemistry to predict the ground and excited state properties of complex chemical systems has driven the development of computational quantum chemistry software, integrating advances in theory, applied mathematics, and computer science. The emergence of new computational paradigms associated with exascale technologies also poses significant challenges that require a flexible forward strategy to take full advantage of existing and forthcoming computational resources. In this context, the sustainability and interoperability of computational chemistry software development are among the most pressing issues. In this perspective, we discuss software infrastructure needs and investments with an eye to fully utilize exascale resources and provide unique computational tools for next-generation science problems and scientific discoveries.
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Affiliation(s)
- Rosa Di Felice
- Departments
of Physics and Astronomy and Quantitative and Computational Biology, University of Southern California, Los Angeles, California 90089, United States
- CNR-NANO
Modena, Modena 41125, Italy
| | - Maricris L. Mayes
- Department
of Chemistry and Biochemistry, University
of Massachusetts Dartmouth, North Dartmouth, Massachusetts 02747, United States
| | | | | | - Garnet Kin-Lic Chan
- Division
of Chemistry and Chemical Engineering, California
Institute of Technology, Pasadena, California 91125, United States
| | - Wibe A. de Jong
- Lawrence
Berkeley National Laboratory, Berkeley, California 94720, United States
| | - Niranjan Govind
- Physical
Sciences Division, Pacific Northwest National
Laboratory, Richland, Washington 99354, United States
| | - Martin Head-Gordon
- Pitzer Center
for Theoretical Chemistry, Department of Chemistry, University of California, Berkeley, California 94720, United States
| | - Matthew R. Hermes
- Department
of Chemistry, Chicago Center for Theoretical Chemistry, University of Chicago, Chicago, Illinois 60637, United States
| | - Karol Kowalski
- Physical
Sciences Division, Pacific Northwest National
Laboratory, Richland, Washington 99354, United States
| | - Xiaosong Li
- Department
of Chemistry, University of Washington, Seattle, Washington 98195, United States
| | - Hans Lischka
- Department
of Chemistry and Biochemistry, Texas Tech
University, Lubbock, Texas 79409, United States
| | - Karl T. Mueller
- Physical
and Computational Sciences Directorate, Pacific Northwest National Laboratory, Richland, Washington 99354, United States
| | - Erdal Mutlu
- Advanced
Computing, Mathematics, and Data Division, Pacific Northwest National Laboratory, Richland, Washington 99354, United States
| | - Anders M. N. Niklasson
- Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States
| | - Mark R. Pederson
- Department
of Physics, The University of Texas at El
Paso, El Paso, Texas 79968, United States
| | - Bo Peng
- Physical
Sciences Division, Pacific Northwest National
Laboratory, Richland, Washington 99354, United States
| | - Ron Shepard
- Chemical
Sciences and Engineering Division, Argonne
National Laboratory, Lemont, Illinois 60439, United States
| | - Edward F. Valeev
- Department
of Chemistry, Virginia Tech, Blacksburg, Virginia 24061, United States
| | | | - Bess Vlaisavljevich
- Department
of Chemistry, University of South Dakota, Vermillion, South Dakota 57069, United States
| | - Theresa L. Windus
- Department
of Chemistry, Iowa State University and
Ames Laboratory, Ames, Iowa 50011, United States
| | - Sotiris S. Xantheas
- Department
of Chemistry, University of Washington, Seattle, Washington 98195, United States
- Advanced
Computing, Mathematics and Data Division, Pacific Northwest National Laboratory, Richland, Washington 99354, United States
| | - Xing Zhang
- Division
of Chemistry and Chemical Engineering, California
Institute of Technology, Pasadena, California 91125, United States
| | - Paul M. Zimmerman
- Department
of Chemistry, University of Michigan, Ann Arbor, Michigan 48109, United States
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8
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Ghiringhelli LM, Baldauf C, Bereau T, Brockhauser S, Carbogno C, Chamanara J, Cozzini S, Curtarolo S, Draxl C, Dwaraknath S, Fekete Á, Kermode J, Koch CT, Kühbach M, Ladines AN, Lambrix P, Himmer MO, Levchenko SV, Oliveira M, Michalchuk A, Miller RE, Onat B, Pavone P, Pizzi G, Regler B, Rignanese GM, Schaarschmidt J, Scheidgen M, Schneidewind A, Sheveleva T, Su C, Usvyat D, Valsson O, Wöll C, Scheffler M. Shared metadata for data-centric materials science. Sci Data 2023; 10:626. [PMID: 37709811 PMCID: PMC10502089 DOI: 10.1038/s41597-023-02501-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Accepted: 08/23/2023] [Indexed: 09/16/2023] Open
Affiliation(s)
- Luca M Ghiringhelli
- Physics Department and IRIS Adlershof, Humboldt-Universität zu Berlin, Berlin, Germany.
- The NOMAD Laboratory at the Fritz-Haber-Institut of the Max-Planck-Gesellschaft and IRIS-Adlershof of the Humboldt-Universität zu Berlin, Berlin, Germany.
- Department of Materials Science and Engineering, Friedrich-Alexander Universität, Erlangen-Nürnberg, Germany.
| | - Carsten Baldauf
- Fritz-Haber-Institut of the Max-Planck-Gesellschaft, Berlin, Germany
| | - Tristan Bereau
- Van't Hoff Institute for Molecular Sciences and Informatics Institute, University of Amsterdam, Amsterdam, 1098 XH, The Netherlands
| | - Sandor Brockhauser
- Physics Department and IRIS Adlershof, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Christian Carbogno
- The NOMAD Laboratory at the Fritz-Haber-Institut of the Max-Planck-Gesellschaft and IRIS-Adlershof of the Humboldt-Universität zu Berlin, Berlin, Germany
| | - Javad Chamanara
- TIB - Leibniz Information Centre for Science and Technology and University Library, 30167, Hanover, Germany
| | - Stefano Cozzini
- AREA Science Park, località Padriciano, 34149, Trieste, Italy
| | - Stefano Curtarolo
- Center for Autonomous Materials Design and Department of Mechanical Engineering and Materials Science, Duke University, Durham, NC, 27708, USA
| | - Claudia Draxl
- Physics Department and IRIS Adlershof, Humboldt-Universität zu Berlin, Berlin, Germany
- The NOMAD Laboratory at the Fritz-Haber-Institut of the Max-Planck-Gesellschaft and IRIS-Adlershof of the Humboldt-Universität zu Berlin, Berlin, Germany
| | | | - Ádám Fekete
- Physics Department and IRIS Adlershof, Humboldt-Universität zu Berlin, Berlin, Germany
| | - James Kermode
- Warwick Centre for Predictive Modelling, School of Engineering, University of Warwick, Coventry, CV4 7AL, United Kingdom
| | - Christoph T Koch
- Physics Department and IRIS Adlershof, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Markus Kühbach
- Physics Department and IRIS Adlershof, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Alvin Noe Ladines
- Physics Department and IRIS Adlershof, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Patrick Lambrix
- Department of Computer and Information Science and The Swedish e-Science Research Centre, Linköping University, Linköping, Sweden
| | - Maja-Olivia Himmer
- The NOMAD Laboratory at the Fritz-Haber-Institut of the Max-Planck-Gesellschaft and IRIS-Adlershof of the Humboldt-Universität zu Berlin, Berlin, Germany
| | - Sergey V Levchenko
- Center for Energy Science and Technology, Skolkovo Institute of Science and Technology, Moscow, Russia
| | - Micael Oliveira
- Max Planck Institute for the Structure and Dynamics of Matter, Hamburg, Germany
| | - Adam Michalchuk
- Federal Institute for Materials Research and Testing (BAM), 12489, Berlin, Germany
- School of Chemistry, University of Birmingham, B15 2TT, Edgbaston, Birmingham, UK
| | - Ronald E Miller
- Department of Mechanical and Aerospace Engineering, Carleton University, Ottawa, ON, K1S 5B6, Canada
| | - Berk Onat
- Warwick Centre for Predictive Modelling, School of Engineering, University of Warwick, Coventry, CV4 7AL, United Kingdom
| | - Pasquale Pavone
- Physics Department and IRIS Adlershof, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Giovanni Pizzi
- Theory and Simulation of Materials (THEOS) and National Centre for Computational Design and Discovery of Novel Materials (MARVEL), École Polytechnique Fédérale de Lausanne, CH-1015, Lausanne, Switzerland
- Laboratory for Materials Simulations (LMS), Paul Scherrer Institut (PSI), CH-5232, Villigen, Switzerland
| | - Benjamin Regler
- The NOMAD Laboratory at the Fritz-Haber-Institut of the Max-Planck-Gesellschaft and IRIS-Adlershof of the Humboldt-Universität zu Berlin, Berlin, Germany
| | - Gian-Marco Rignanese
- Institute of Condensed Matter and Nanosciences (IMCN), UCLouvain, Chemin des Étoiles 8, B-1348, Louvain-la-Neuve, Belgium
| | - Jörg Schaarschmidt
- Institute of Nanotechnology, Karlsruhe Institute of Technology (KIT), 76344 Eggenstein-Leopoldshafen, Karlsruhe, Germany
| | - Markus Scheidgen
- Physics Department and IRIS Adlershof, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Astrid Schneidewind
- Jülich Center for Neutron Science at MLZ, Forschungszentrum Jülich GmbH, Lichtenbergstrase 1, 85748, Garching, Germany
| | - Tatyana Sheveleva
- TIB - Leibniz Information Centre for Science and Technology and University Library, 30167, Hanover, Germany
| | - Chuanxun Su
- CAS Key Laboratory of Quantum Information, University of Science and Technology of China, Hefei, 230026, People's Republic of China
| | - Denis Usvyat
- Chemistry Department, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Omar Valsson
- Department of Chemistry, University of North Texas, Denton, TX, 76201, USA
| | - Christof Wöll
- Institute of Functional Interfaces, Karlsruhe Institute of Technology (KIT), 76344 Eggenstein-Leopoldshafen, Karlsruhe, Germany
| | - Matthias Scheffler
- Physics Department and IRIS Adlershof, Humboldt-Universität zu Berlin, Berlin, Germany
- The NOMAD Laboratory at the Fritz-Haber-Institut of the Max-Planck-Gesellschaft and IRIS-Adlershof of the Humboldt-Universität zu Berlin, Berlin, Germany
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9
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Posenitskiy E, Chilkuri VG, Ammar A, Hapka M, Pernal K, Shinde R, Landinez Borda EJ, Filippi C, Nakano K, Kohulák O, Sorella S, de Oliveira Castro P, Jalby W, Ríos PL, Alavi A, Scemama A. TREXIO: A file format and library for quantum chemistry. J Chem Phys 2023; 158:2888846. [PMID: 37144717 DOI: 10.1063/5.0148161] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Accepted: 04/04/2023] [Indexed: 05/06/2023] Open
Abstract
TREXIO is an open-source file format and library developed for the storage and manipulation of data produced by quantum chemistry calculations. It is designed with the goal of providing a reliable and efficient method of storing and exchanging wave function parameters and matrix elements, making it an important tool for researchers in the field of quantum chemistry. In this work, we present an overview of the TREXIO file format and library. The library consists of a front-end implemented in the C programming language and two different back-ends: a text back-end and a binary back-end utilizing the hierarchical data format version 5 library, which enables fast read and write operations. It is compatible with a variety of platforms and has interfaces for Fortran, Python, and OCaml programming languages. In addition, a suite of tools have been developed to facilitate the use of the TREXIO format and library, including converters for popular quantum chemistry codes and utilities for validating and manipulating data stored in TREXIO files. The simplicity, versatility, and ease of use of TREXIO make it a valuable resource for researchers working with quantum chemistry data.
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Affiliation(s)
- Evgeny Posenitskiy
- Laboratoire de Chimie et Physique Quantiques - UMR5626, CNRS/Université Paul Sabatier, Bat. 3R1b4, 118 route de Narbonne, 31062 Toulouse Cedex 09, France
- Qubit Pharmaceuticals, Incubateur Paris Biotech Santé, 24 Rue du Faubourg Saint Jacques, 75014 Paris, France
| | - Vijay Gopal Chilkuri
- Laboratoire de Chimie et Physique Quantiques - UMR5626, CNRS/Université Paul Sabatier, Bat. 3R1b4, 118 route de Narbonne, 31062 Toulouse Cedex 09, France
- Institut des Sciences Moléculaires de Marseille, Service 561, Campus Scientifique de St. Jérôme, Aix Marseille Université, Centrale Marseille 13, 397 Marseille Cedex 20, France
| | - Abdallah Ammar
- Laboratoire de Chimie et Physique Quantiques - UMR5626, CNRS/Université Paul Sabatier, Bat. 3R1b4, 118 route de Narbonne, 31062 Toulouse Cedex 09, France
| | - Michał Hapka
- Faculty of Chemistry, University of Warsaw, ul. L. Pasteura 1, 02-093 Warsaw, Poland
| | - Katarzyna Pernal
- Institute of Physics, Lodz University of Technology, ul. Wolczanska 217/221, 93-005 Lodz, Poland
| | - Ravindra Shinde
- MESA+ Institute for Nanotechnology, University of Twente, P.O. Box 217, 7500 AE Enschede, The Netherlands
| | - Edgar Josué Landinez Borda
- MESA+ Institute for Nanotechnology, University of Twente, P.O. Box 217, 7500 AE Enschede, The Netherlands
| | - Claudia Filippi
- MESA+ Institute for Nanotechnology, University of Twente, P.O. Box 217, 7500 AE Enschede, The Netherlands
| | - Kosuke Nakano
- Research and Services Division of Materials Data and Integrated System, National Institute for Materials Science (NIMS), Tsukuba, Ibaraki 305-0047, Japan
- International School for Advanced Studies (SISSA), Via Bonomea 265, 34136 Trieste, Italy
| | - Otto Kohulák
- Laboratoire de Chimie et Physique Quantiques - UMR5626, CNRS/Université Paul Sabatier, Bat. 3R1b4, 118 route de Narbonne, 31062 Toulouse Cedex 09, France
- International School for Advanced Studies (SISSA), Via Bonomea 265, 34136 Trieste, Italy
| | - Sandro Sorella
- International School for Advanced Studies (SISSA), Via Bonomea 265, 34136 Trieste, Italy
| | | | - William Jalby
- Université Paris-Saclay, UVSQ, LI-PaRAD, 9 Boulevard d'Alembert, 78280 Guyancourt, France
| | - Pablo López Ríos
- Max Planck Institute for Solid State Research, Heisenbergstrasse 1, 70569 Stuttgart, Germany
| | - Ali Alavi
- Max Planck Institute for Solid State Research, Heisenbergstrasse 1, 70569 Stuttgart, Germany
| | - Anthony Scemama
- Laboratoire de Chimie et Physique Quantiques - UMR5626, CNRS/Université Paul Sabatier, Bat. 3R1b4, 118 route de Narbonne, 31062 Toulouse Cedex 09, France
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10
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Lebedeva IV, García A, Artacho E, Ordejón P. Modular implementation of the linear- and cubic-scaling orbital minimization methods in electronic structure codes using atomic orbitals. ROYAL SOCIETY OPEN SCIENCE 2023; 10:230063. [PMID: 37122948 PMCID: PMC10130719 DOI: 10.1098/rsos.230063] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Accepted: 03/29/2023] [Indexed: 05/03/2023]
Abstract
We present a code modularization approach to design efficient and massively parallel cubic- and linear-scaling solvers for electronic structure calculations using atomic orbitals. The modular implementation of the orbital minimization method, in which linear algebra and parallelization issues are handled via external libraries, is demonstrated in the SIESTA code. The distributed block compressed sparse row (DBCSR) and scalable linear algebra package (ScaLAPACK) libraries are used for algebraic operations with sparse and dense matrices, respectively. The MatrixSwitch and libOMM libraries, recently developed within the Electronic Structure Library, facilitate switching between different matrix formats and implement the energy minimization. We show results comparing the performance of several cubic-scaling algorithms, and also demonstrate the parallel performance of the linear-scaling solvers, and their supremacy over the cubic-scaling solvers for insulating systems with sizes of several hundreds of atoms.
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Affiliation(s)
- Irina V. Lebedeva
- CIC nanoGUNE BRTA, Donostia-San Sebastián 20018, Spain
- Catalan Institute of Nanoscience and Nanotechnology—ICN2 (CSIC and BIST), Campus UAB, Bellaterra 08193, Spain
- Simune Atomistics, Avenida de Tolosa 76, Donostia-San Sebastián 20018, Spain
| | - Alberto García
- Institut de Ciència de Materials de Barcelona (ICMAB-CSIC), Bellaterra 08193, Spain
| | - Emilio Artacho
- CIC nanoGUNE BRTA, Donostia-San Sebastián 20018, Spain
- Donostia International Physics Center DIPC, Donostia-San Sebastián 20018, Spain
- Theory of Condensed Matter, Cavendish Laboratory, University of Cambridge, Cambridge CB3 0HE, UK
- Ikerbasque, Basque Foundation for Science, Bilbao 48011, Spain
| | - Pablo Ordejón
- Catalan Institute of Nanoscience and Nanotechnology—ICN2 (CSIC and BIST), Campus UAB, Bellaterra 08193, Spain
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11
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Lehtola S, Karttunen AJ. Free and open source software for computational chemistry education. WIRES COMPUTATIONAL MOLECULAR SCIENCE 2022. [DOI: 10.1002/wcms.1610] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Affiliation(s)
- Susi Lehtola
- Molecular Sciences Software Institute Blacksburg Virginia USA
| | - Antti J. Karttunen
- Department of Chemistry and Materials Science Aalto University Espoo Finland
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12
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Trepte K, Schwalbe S, Liebing S, Schulze WT, Kortus J, Myneni H, Ivanov AV, Lehtola S. Chemical bonding theories as guides for self-interaction corrected solutions: Multiple local minima and symmetry breaking. J Chem Phys 2021; 155:224109. [PMID: 34911315 DOI: 10.1063/5.0071796] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Abstract
Fermi-Löwdin orbitals (FLOs) are a special set of localized orbitals, which have become commonly used in combination with the Perdew-Zunger self-interaction correction (SIC) in the FLO-SIC method. The FLOs are obtained for a set of occupied orbitals by specifying a classical position for each electron. These positions are known as Fermi-orbital descriptors (FODs), and they have a clear relation to chemical bonding. In this study, we show how FLOs and FODs can be used to initialize, interpret, and justify SIC solutions in a common chemical picture, both within FLO-SIC and in traditional variational SIC, and to locate distinct local minima in either of these approaches. We demonstrate that FLOs based on Lewis theory lead to symmetry breaking for benzene-the electron density is found to break symmetry already at the symmetric molecular structure-while ones from Linnett's double-quartet theory reproduce symmetric electron densities and molecular geometries. Introducing a benchmark set of 16 planar cyclic molecules, we show that using Lewis theory as the starting point can lead to artifactual dipole moments of up to 1 D, while Linnett SIC dipole moments are in better agreement with experimental values. We suggest using the dipole moment as a diagnostic of symmetry breaking in SIC and monitoring it in all SIC calculations. We show that Linnett structures can often be seen as superpositions of Lewis structures and propose Linnett structures as a simple way to describe aromatic systems in SIC with reduced symmetry breaking. The role of hovering FODs is also briefly discussed.
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Affiliation(s)
- Kai Trepte
- SUNCAT Center for Interface Science and Catalysis, Stanford University, Menlo Park, California 94025, USA
| | - Sebastian Schwalbe
- Institute of Theoretical Physics, TU Bergakademie Freiberg, D-09599 Freiberg, Germany
| | - Simon Liebing
- Joint Institute for Nuclear Research Dubna, Bogoliubov Laboratory of Theoretical Physics, 141980 Dubna, Russia
| | - Wanja T Schulze
- Institute of Theoretical Physics, TU Bergakademie Freiberg, D-09599 Freiberg, Germany
| | - Jens Kortus
- Institute of Theoretical Physics, TU Bergakademie Freiberg, D-09599 Freiberg, Germany
| | - Hemanadhan Myneni
- Science Institute and Faculty of Physical Sciences, VR-III, University of Iceland, 107 Reykjavík, Iceland
| | - Aleksei V Ivanov
- Science Institute and Faculty of Physical Sciences, VR-III, University of Iceland, 107 Reykjavík, Iceland
| | - Susi Lehtola
- Molecular Sciences Software Institute, Blacksburg, Virginia 24061, USA
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13
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Andrade X, Pemmaraju CD, Kartsev A, Xiao J, Lindenberg A, Rajpurohit S, Tan LZ, Ogitsu T, Correa AA. Inq, a Modern GPU-Accelerated Computational Framework for (Time-Dependent) Density Functional Theory. J Chem Theory Comput 2021; 17:7447-7467. [PMID: 34726888 DOI: 10.1021/acs.jctc.1c00562] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
We present inq, a new implementation of density functional theory (DFT) and time-dependent DFT (TDDFT) written from scratch to work on graphic processing units (GPUs). Besides GPU support, inq makes use of modern code design features and takes advantage of newly available hardware. By designing the code around algorithms, rather than against specific implementations and numerical libraries, we aim to provide a concise and modular code. The result is a fairly complete DFT/TDDFT implementation in roughly 12 000 lines of open-source C++ code representing a modular platform for community-driven application development on emerging high-performance computing architectures.
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Affiliation(s)
- Xavier Andrade
- Quantum Simulations Group, Lawrence Livermore National Laboratory, Livermore, California 94551, United States
| | - Chaitanya Das Pemmaraju
- Stanford Institute for Materials and Energy Sciences, SLAC National Accelerator Laboratory, Menlo Park, California 94025, United States
| | - Alexey Kartsev
- Stanford Institute for Materials and Energy Sciences, SLAC National Accelerator Laboratory, Menlo Park, California 94025, United States
| | - Jun Xiao
- Stanford Institute for Materials and Energy Sciences, SLAC National Accelerator Laboratory, Menlo Park, California 94025, United States
| | - Aaron Lindenberg
- Stanford Institute for Materials and Energy Sciences, SLAC National Accelerator Laboratory, Menlo Park, California 94025, United States
| | - Sangeeta Rajpurohit
- The Molecular Foundry, Lawrence Berkeley National Laboratory, Berkeley, California 94720, United States
| | - Liang Z Tan
- The Molecular Foundry, Lawrence Berkeley National Laboratory, Berkeley, California 94720, United States
| | - Tadashi Ogitsu
- Quantum Simulations Group, Lawrence Livermore National Laboratory, Livermore, California 94551, United States
| | - Alfredo A Correa
- Quantum Simulations Group, Lawrence Livermore National Laboratory, Livermore, California 94551, United States
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14
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Lemmens L, De Vriendt X, Tolstykh D, Huysentruyt T, Bultinck P, Acke G. GQCP: The Ghent Quantum Chemistry Package. J Chem Phys 2021; 155:084802. [PMID: 34470369 DOI: 10.1063/5.0057515] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023] Open
Abstract
The Ghent Quantum Chemistry Package (GQCP) is an open-source electronic structure software package that aims to provide an intuitive and expressive software framework for electronic structure software development. Its high-level interfaces (accessible through C++ and Python) have been specifically designed to correspond to theoretical concepts, while retaining access to lower-level intermediates and allowing structural run-time modifications of quantum chemical solvers. GQCP focuses on providing quantum chemical method developers with the computational "building blocks" that allow them to flexibly develop proof of principle implementations for new methods and applications up to the level of two-component spinor bases.
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Affiliation(s)
- Laurent Lemmens
- Ghent Quantum Chemistry Group, Department of Chemistry, Ghent University, Krijgslaan 281 (S3), B-9000 Gent, Belgium
| | - Xeno De Vriendt
- Ghent Quantum Chemistry Group, Department of Chemistry, Ghent University, Krijgslaan 281 (S3), B-9000 Gent, Belgium
| | - Daria Tolstykh
- Ghent Quantum Chemistry Group, Department of Chemistry, Ghent University, Krijgslaan 281 (S3), B-9000 Gent, Belgium
| | - Tobias Huysentruyt
- Ghent Quantum Chemistry Group, Department of Chemistry, Ghent University, Krijgslaan 281 (S3), B-9000 Gent, Belgium
| | - Patrick Bultinck
- Ghent Quantum Chemistry Group, Department of Chemistry, Ghent University, Krijgslaan 281 (S3), B-9000 Gent, Belgium
| | - Guillaume Acke
- Ghent Quantum Chemistry Group, Department of Chemistry, Ghent University, Krijgslaan 281 (S3), B-9000 Gent, Belgium
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15
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Westermayr J, Gastegger M, Schütt KT, Maurer RJ. Perspective on integrating machine learning into computational chemistry and materials science. J Chem Phys 2021; 154:230903. [PMID: 34241249 DOI: 10.1063/5.0047760] [Citation(s) in RCA: 67] [Impact Index Per Article: 22.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023] Open
Abstract
Machine learning (ML) methods are being used in almost every conceivable area of electronic structure theory and molecular simulation. In particular, ML has become firmly established in the construction of high-dimensional interatomic potentials. Not a day goes by without another proof of principle being published on how ML methods can represent and predict quantum mechanical properties-be they observable, such as molecular polarizabilities, or not, such as atomic charges. As ML is becoming pervasive in electronic structure theory and molecular simulation, we provide an overview of how atomistic computational modeling is being transformed by the incorporation of ML approaches. From the perspective of the practitioner in the field, we assess how common workflows to predict structure, dynamics, and spectroscopy are affected by ML. Finally, we discuss how a tighter and lasting integration of ML methods with computational chemistry and materials science can be achieved and what it will mean for research practice, software development, and postgraduate training.
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Affiliation(s)
- Julia Westermayr
- Department of Chemistry, University of Warwick, Gibbet Hill Road, Coventry CV4 7AL, United Kingdom
| | - Michael Gastegger
- Machine Learning Group, Technische Universität Berlin, 10587 Berlin, Germany
| | - Kristof T Schütt
- Machine Learning Group, Technische Universität Berlin, 10587 Berlin, Germany
| | - Reinhard J Maurer
- Department of Chemistry, University of Warwick, Gibbet Hill Road, Coventry CV4 7AL, United Kingdom
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16
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Rossi M. Progress and challenges in ab initio simulations of quantum nuclei in weakly bonded systems. J Chem Phys 2021; 154:170902. [PMID: 34241065 DOI: 10.1063/5.0042572] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Abstract
Atomistic simulations based on the first-principles of quantum mechanics are reaching unprecedented length scales. This progress is due to the growth in computational power allied with the development of new methodologies that allow the treatment of electrons and nuclei as quantum particles. In the realm of materials science, where the quest for desirable emergent properties relies increasingly on soft weakly bonded materials, such methods have become indispensable. In this Perspective, an overview of simulation methods that are applicable for large system sizes and that can capture the quantum nature of electrons and nuclei in the adiabatic approximation is given. In addition, the remaining challenges are discussed, especially regarding the inclusion of nuclear quantum effects (NQEs) beyond a harmonic or perturbative treatment, the impact of NQEs on electronic properties of weakly bonded systems, and how different first-principles potential energy surfaces can change the impact of NQEs on the atomic structure and dynamics of weakly bonded systems.
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Affiliation(s)
- Mariana Rossi
- Max Planck Institute for the Structure and Dynamics of Matter, Luruper Chaussee 149, 22761 Hamburg, Germany
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17
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Sherrill CD, Manolopoulos DE, Martínez TJ, Michaelides A. Electronic structure software. J Chem Phys 2020; 153:070401. [DOI: 10.1063/5.0023185] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Affiliation(s)
- C. David Sherrill
- Center for Computational Molecular Science and Technology, School of Chemistry and Biochemistry, and School of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, Georgia
30332-0400, USA
| | - David E. Manolopoulos
- Physical and Theoretical Chemistry Laboratory, Department of Chemistry, Oxford University, South Parks Road, Oxford OX1
3QZ, United Kingdom
| | - Todd J. Martínez
- Department of Chemistry and the PULSE Institute, Stanford University, Stanford, California 94305,
USA
| | - Angelos Michaelides
- Thomas Young Centre, London Centre for Nanotechnology and Department of Physics and Astronomy, University College London, Gower Street, London WC1E 6BT, United Kingdom
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