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Title: Density functional theory of water with the machine-learned DM21 functional

Abstract

The delicate interplay between functional-driven and density-driven errors in density functional theory (DFT) has hindered traditional density functional approximations (DFAs) from providing an accurate description of water for over 30 years. Recently, the deep-learned DeepMind 21 (DM21) functional has been shown to overcome the limitations of traditional DFAs as it is free of delocalization error. To determine if DM21 can enable a molecular-level description of the physical properties of aqueous systems within Kohn–Sham DFT, we assess the accuracy of the DM21 functional for neutral, protonated, and deprotonated water clusters. Furthermore, we find that the ability of DM21 to accurately predict the energetics of aqueous clusters varies significantly with cluster size. Additionally, we introduce the many-body MB-DM21 potential derived from DM21 data within the many-body expansion of the energy and use it in simulations of liquid water as a function of temperature at ambient pressure. We find that size-dependent functional-driven errors identified in the analysis of the energetics of small clusters calculated with the DM21 functional result in the MB-DM21 potential systematically overestimating the hydrogen-bond strength and, consequently, predicting a more ice-like local structure of water at room temperature.

Authors:
ORCiD logo [1];  [1]; ORCiD logo [1]; ORCiD logo [1]
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  1. University of California, San Diego, CA (United States)
Publication Date:
Research Org.:
Univ. of California, San Diego, CA (United States); Univ. of California, Oakland, CA (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Basic Energy Sciences (BES); National Science Foundation (NSF)
OSTI Identifier:
1979083
Alternate Identifier(s):
OSTI ID: 1865223
Grant/Contract Number:  
SC0019490; AC02-05CH11231; ACI-1548562
Resource Type:
Accepted Manuscript
Journal Name:
Journal of Chemical Physics
Additional Journal Information:
Journal Volume: 156; Journal Issue: 16; Journal ID: ISSN 0021-9606
Publisher:
American Institute of Physics (AIP)
Country of Publication:
United States
Language:
English
Subject:
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY; 71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSICS; Chemistry; Physics; Water model; Density functional theory; Molecular dynamics; Quantum effects; Many body systems; Thermodynamic properties; Machine learning; Intermolecular forces; Delocalization; Chemical bonding

Citation Formats

Palos, Etienne, Lambros, Eleftherios, Dasgupta, Saswata, and Paesani, Francesco. Density functional theory of water with the machine-learned DM21 functional. United States: N. p., 2022. Web. doi:10.1063/5.0090862.
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Palos, Etienne, Lambros, Eleftherios, Dasgupta, Saswata, & Paesani, Francesco. Density functional theory of water with the machine-learned DM21 functional. United States. https://doi.org/10.1063/5.0090862
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Palos, Etienne, Lambros, Eleftherios, Dasgupta, Saswata, and Paesani, Francesco. Wed . "Density functional theory of water with the machine-learned DM21 functional". United States. https://doi.org/10.1063/5.0090862. https://www.osti.gov/servlets/purl/1979083.
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@article{osti_1979083,
title = {Density functional theory of water with the machine-learned DM21 functional},
author = {Palos, Etienne and Lambros, Eleftherios and Dasgupta, Saswata and Paesani, Francesco},
abstractNote = {The delicate interplay between functional-driven and density-driven errors in density functional theory (DFT) has hindered traditional density functional approximations (DFAs) from providing an accurate description of water for over 30 years. Recently, the deep-learned DeepMind 21 (DM21) functional has been shown to overcome the limitations of traditional DFAs as it is free of delocalization error. To determine if DM21 can enable a molecular-level description of the physical properties of aqueous systems within Kohn–Sham DFT, we assess the accuracy of the DM21 functional for neutral, protonated, and deprotonated water clusters. Furthermore, we find that the ability of DM21 to accurately predict the energetics of aqueous clusters varies significantly with cluster size. Additionally, we introduce the many-body MB-DM21 potential derived from DM21 data within the many-body expansion of the energy and use it in simulations of liquid water as a function of temperature at ambient pressure. We find that size-dependent functional-driven errors identified in the analysis of the energetics of small clusters calculated with the DM21 functional result in the MB-DM21 potential systematically overestimating the hydrogen-bond strength and, consequently, predicting a more ice-like local structure of water at room temperature.},
doi = {10.1063/5.0090862},
journal = {Journal of Chemical Physics},
number = 16,
volume = 156,
place = {United States},
year = {Wed Apr 27 00:00:00 EDT 2022},
month = {Wed Apr 27 00:00:00 EDT 2022}
}
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