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Herman KM, Stone AJ, Xantheas SS. Accurate Calculation of Many-Body Energies in Water Clusters Using a Classical Geometry-Dependent Induction Model. J Chem Theory Comput 2023; 19:6805-6815. [PMID: 37703063 DOI: 10.1021/acs.jctc.3c00575] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/14/2023]
Abstract
We incorporate geometry-dependent distributed multipole and polarizability surfaces into an induction model that is used to describe the 3- and 4-body terms of the interaction between water molecules. The moment expansion is carried out up to the hexadecapole with the multipoles distributed on the atom sites. Dipole-dipole, dipole-quadrupole, and quadrupole-quadrupole distributed polarizabilities are used to represent the response of the multipoles to an electric field. We compare the model against two large databases consisting of 43,844 3-body terms and 3,603 4-body terms obtained from high level ab initio calculations previously used to fit the MB-pol and q-AQUA classical interaction potentials for water. The classical induction model with no adjustable parameters reproduces the ab initio 3-/4-body terms contained in these two databases with a root-mean-square error (RMSE) of 0.104/0.058 and a mean-absolute error (MAE) of 0.054/0.026 kcal/mol, respectively. These results are on par with the ones obtained by fitting the same data using over 14,000 (for the 3-body) and 200 (for the 4-body) parameters via Permutationally Invariant Polynomials (PIPs). This demonstrates the accuracy of this physically motivated model in describing the 3- and 4-body terms in the interactions between water molecules with no adjustable parameters. The triple-dipole-dispersion energy, included in the calculation of the 3-body energy, was found to be small but not quite negligible. The model represents a practical, efficient, and transferable approach for obtaining accurate nonadditive interactions for multicomponent systems without the need to perform tens of thousands of high level electronic structure calculations and fitting them with PIPs.
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Affiliation(s)
- Kristina M Herman
- Department of Chemistry, University of Washington, Seattle, Washington 98185, United States
| | - Anthony J Stone
- Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, U.K
| | - Sotiris S Xantheas
- Department of Chemistry, University of Washington, Seattle, Washington 98185, United States
- Advanced Computing Mathematics and Data Division, Pacific Northwest National Laboratory, 902 Battelle Boulevard, P.O. Box 999, MSIN J7-10, Richland, Washington 99352, United States
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Herman KM, Xantheas SS. An extensive assessment of the performance of pairwise and many-body interaction potentials in reproducing ab initio benchmark binding energies for water clusters n = 2-25. Phys Chem Chem Phys 2023; 25:7120-7143. [PMID: 36853239 DOI: 10.1039/d2cp03241d] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
We assess the performance of 7 pairwise additive (TIP3P, TIP4P, TIP4P-ice, TIP5P, OPC, SPC, SPC/E) and 8 families of many-body potentials (q-AQUA, HIPPO, AMOEBA, EFP, TTM, WHBB, MB-pol, MB-UCB) in reproducing high-level ab initio benchmark values, CCSD(T) or MP2 at the complete basis set (CBS) limit for the binding energy and the many-body expansion (MBE) of water clusters n = 2-11, 16-17, 20, 25. By including a large range of cluster sizes having dissimilar hydrogen bonding networks, we obtain an understanding of how these potentials perform for different hydrogen bonding arrangements that are mostly outside of their parameterization range. While it is appropriate to compare the results of ab initio based many-body potentials directly to the electronic binding energies (De's), the pairwise additive ones are compared to the enthalpies at T = 298 K, ΔH(298 K), as the latter class of force fields are parametrized to reproduce enthalpies (implicitly accounting for zero-point energy corrections) rather than binding energies. We find that all pairwise additive potentials considered overestimate the reference ΔH values for the n = 2-25 clusters by >13%. For the water dimer (n = 2) in particular, the errors are in the range 83-119% for the pairwise additive potentials studied since these are based on an effective rather than the true 2-body interaction specifically designed as a means of partially accounting for the missing many-body terms. This stronger 2-body interaction is achieved by an enhanced monomer dipole moment that mimics its increase from the gas phase monomer to the condensed phase value. Indeed, for cluster sizes n ≥ 4 the percent deviations become slightly smaller (albeit all exceeding 13%). In contrast, we find that the many-body potentials perform more accurately in reproducing the electronic binding energies (De's) throughout the entire cluster range (n = 2-25), all reproducing the ab initio benchmark binding energies within ±7% of the respective CBS values. We further assess the ability of a subset of the many-body potentials (MB-UCB, q-AQUA, MB-pol, and TTM2.1-F) to also reproduce the magnitude of the ab initio many-body energy terms for water cluster sizes n = 7, 10, 16 and 17. The potentials show an overall good agreement with the available benchmark values. However, we identify characteristic differences upon comparing the many-body terms at both the ab initio-optimized geometries and the respective potential-optimized geometries to the reference ab initio values. Additionally, by applying this analysis to a wide range of cluster sizes, trends in the MBE of the potentials with increasing cluster size can be identified. Finally, in an attempt to draw a parallel between the pairwise additive and many-body potentials, we report the analysis of the individual molecular dipole moments for water clusters with 1 to ∼4 solvation shells with the TTM2.1-F potential. We find that the internally solvated water molecules have in general a larger molecular dipole moment ranging from 2.6-3.0 D. This justifies the use of an enhanced, with respect to the gas-phase value, molecular dipole moment for the pairwise additive potentials, which is intended to fold in the many body terms into an effective (enhanced) pairwise interaction through the choice of the charges. These results have important implications for the development of future generations of efficient, transferable, and highly accurate classical interaction potentials in both the pairwise additive and many-body categories.
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Affiliation(s)
- Kristina M Herman
- Department of Chemistry, University of Washington, Seattle, WA 98195, USA.
| | - Sotiris S Xantheas
- Department of Chemistry, University of Washington, Seattle, WA 98195, USA. .,Advanced Computing, Mathematics and Data Division, Pacific Northwest National Laboratory, 902 Battelle Boulevard, P.O. Box 999, MS K1-83, WA, 99352, USA.
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Barca GMJ, Bertoni C, Carrington L, Datta D, De Silva N, Deustua JE, Fedorov DG, Gour JR, Gunina AO, Guidez E, Harville T, Irle S, Ivanic J, Kowalski K, Leang SS, Li H, Li W, Lutz JJ, Magoulas I, Mato J, Mironov V, Nakata H, Pham BQ, Piecuch P, Poole D, Pruitt SR, Rendell AP, Roskop LB, Ruedenberg K, Sattasathuchana T, Schmidt MW, Shen J, Slipchenko L, Sosonkina M, Sundriyal V, Tiwari A, Galvez Vallejo JL, Westheimer B, Włoch M, Xu P, Zahariev F, Gordon MS. Recent developments in the general atomic and molecular electronic structure system. J Chem Phys 2020; 152:154102. [PMID: 32321259 DOI: 10.1063/5.0005188] [Citation(s) in RCA: 495] [Impact Index Per Article: 123.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
A discussion of many of the recently implemented features of GAMESS (General Atomic and Molecular Electronic Structure System) and LibCChem (the C++ CPU/GPU library associated with GAMESS) is presented. These features include fragmentation methods such as the fragment molecular orbital, effective fragment potential and effective fragment molecular orbital methods, hybrid MPI/OpenMP approaches to Hartree-Fock, and resolution of the identity second order perturbation theory. Many new coupled cluster theory methods have been implemented in GAMESS, as have multiple levels of density functional/tight binding theory. The role of accelerators, especially graphical processing units, is discussed in the context of the new features of LibCChem, as it is the associated problem of power consumption as the power of computers increases dramatically. The process by which a complex program suite such as GAMESS is maintained and developed is considered. Future developments are briefly summarized.
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Affiliation(s)
- Giuseppe M J Barca
- Research School of Computer Science, Australian National University, Canberra, ACT 2601, Australia
| | - Colleen Bertoni
- Argonne Leadership Computing Facility, Argonne National Laboratory, Lemont, Illinois 60439, USA
| | - Laura Carrington
- EP Analytics, 12121 Scripps Summit Dr. Ste. 130, San Diego, California 92131, USA
| | - Dipayan Datta
- Department of Chemistry and Ames Laboratory, Iowa State University, Ames, Iowa 50011, USA
| | - Nuwan De Silva
- Department of Physical and Biological Sciences, Western New England University, Springfield, Massachusetts 01119, USA
| | - J Emiliano Deustua
- Department of Chemistry, Michigan State University, East Lansing, Michigan 48824, USA
| | - Dmitri G Fedorov
- Research Center for Computational Design of Advanced Functional Materials (CD-FMat), National Institute of Advanced Industrial Science and Technology (AIST), Umezono 1-1-1, Tsukuba 305-8568, Japan
| | - Jeffrey R Gour
- Microsoft, 15590 NE 31st St., Redmond, Washington 98052, USA
| | - Anastasia O Gunina
- Department of Chemistry and Ames Laboratory, Iowa State University, Ames, Iowa 50011, USA
| | - Emilie Guidez
- Department of Chemistry, University of Colorado Denver, Denver, Colorado 80217, USA
| | - Taylor Harville
- Department of Chemistry and Ames Laboratory, Iowa State University, Ames, Iowa 50011, USA
| | - Stephan Irle
- Computational Science and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37830, USA
| | - Joe Ivanic
- Advanced Biomedical Computational Science, Frederick National Laboratory for Cancer Research, Frederick, Maryland 21702, USA
| | - Karol Kowalski
- Physical Sciences Division, Battelle, Pacific Northwest National Laboratory, K8-91, P.O. Box 999, Richland, Washington 99352, USA
| | - Sarom S Leang
- EP Analytics, 12121 Scripps Summit Dr. Ste. 130, San Diego, California 92131, USA
| | - Hui Li
- Department of Chemistry, University of Nebraska, Lincoln, Nebraska 68588, USA
| | - Wei Li
- School of Chemistry and Chemical Engineering, Key Laboratory of Mesoscopic Chemistry of Ministry of Education, Institute of Theoretical and Computational Chemistry, Nanjing University, Nanjing 210023, People's Republic of China
| | - Jesse J Lutz
- Center for Computing Research, Sandia National Laboratories, Albuquerque, New Mexico 87185, USA
| | - Ilias Magoulas
- Department of Chemistry, Michigan State University, East Lansing, Michigan 48824, USA
| | - Joani Mato
- Department of Chemistry and Ames Laboratory, Iowa State University, Ames, Iowa 50011, USA
| | - Vladimir Mironov
- Department of Chemistry, Lomonosov Moscow State University, Leninskie Gory 1/3, Moscow 119991, Russian Federation
| | - Hiroya Nakata
- Kyocera Corporation, Research Institute for Advanced Materials and Devices, 3-5-3 Hikaridai Seika-cho, Souraku-gun, Kyoto 619-0237, Japan
| | - Buu Q Pham
- Department of Chemistry and Ames Laboratory, Iowa State University, Ames, Iowa 50011, USA
| | - Piotr Piecuch
- Department of Chemistry, Michigan State University, East Lansing, Michigan 48824, USA
| | - David Poole
- Department of Chemistry and Ames Laboratory, Iowa State University, Ames, Iowa 50011, USA
| | - Spencer R Pruitt
- Department of Chemistry and Ames Laboratory, Iowa State University, Ames, Iowa 50011, USA
| | - Alistair P Rendell
- Research School of Computer Science, Australian National University, Canberra, ACT 2601, Australia
| | - Luke B Roskop
- Cray Inc., a Hewlett Packard Enterprise Company, 2131 Lindau Ln #1000, Bloomington, Minnesota 55425, USA
| | - Klaus Ruedenberg
- Department of Chemistry and Ames Laboratory, Iowa State University, Ames, Iowa 50011, USA
| | | | - Michael W Schmidt
- Department of Chemistry and Ames Laboratory, Iowa State University, Ames, Iowa 50011, USA
| | - Jun Shen
- Department of Chemistry, Michigan State University, East Lansing, Michigan 48824, USA
| | - Lyudmila Slipchenko
- Department of Chemistry, Purdue University, West Lafayette, Indiana 47907, USA
| | - Masha Sosonkina
- Department of Computational Modeling and Simulation Engineering, Old Dominion University, Norfolk, Virginia 23529, USA
| | - Vaibhav Sundriyal
- Department of Computational Modeling and Simulation Engineering, Old Dominion University, Norfolk, Virginia 23529, USA
| | - Ananta Tiwari
- EP Analytics, 12121 Scripps Summit Dr. Ste. 130, San Diego, California 92131, USA
| | - Jorge L Galvez Vallejo
- Department of Chemistry and Ames Laboratory, Iowa State University, Ames, Iowa 50011, USA
| | - Bryce Westheimer
- Department of Chemistry and Ames Laboratory, Iowa State University, Ames, Iowa 50011, USA
| | - Marta Włoch
- 530 Charlesina Dr., Rochester, Michigan 48306, USA
| | - Peng Xu
- Department of Chemistry and Ames Laboratory, Iowa State University, Ames, Iowa 50011, USA
| | - Federico Zahariev
- Department of Chemistry and Ames Laboratory, Iowa State University, Ames, Iowa 50011, USA
| | - Mark S Gordon
- Department of Chemistry and Ames Laboratory, Iowa State University, Ames, Iowa 50011, USA
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