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Title: Transferable Dynamic Molecular Charge Assignment Using Deep Neural Networks

Abstract

We report that the ability to accurately and efficiently compute quantum-mechanical partial atomistic charges has many practical applications, such as calculations of IR spectra, analysis of chemical bonding, and classical force field parametrization. Machine learning (ML) techniques provide a possible avenue for the efficient prediction of atomic partial charges. Modern ML advances in the prediction of molecular energies [i.e., the hierarchical interacting particle neural network (HIP-NN)] has provided the necessary model framework and architecture to predict transferable, extensible, and conformationally dynamic atomic partial charges based on reference density functional theory (DFT) simulations. Utilizing HIP-NN, we show that ML charge prediction can be highly accurate over a wide range of molecules (both small and large) across a variety of charge partitioning schemes such as the Hirshfeld, CM5, MSK, and NBO methods. To demonstrate transferability and size extensibility, we compare ML results with reference DFT calculations on the COMP6 benchmark, achieving errors of 0.004e (elementary charge). This is remarkable since this benchmark contains two proteins that are multiple times larger than the largest molecules in the training set. An application of our atomic charge predictions on nonequilibrium geometries is the generation of IR spectra for organic molecules from dynamical trajectories onmore » a variety of organic molecules, which show good agreement with calculated IR spectra with reference method. Critically, HIP-NN charge predictions are many orders of magnitude faster than direct DFT calculations. Lastly, these combined results provide further evidence that ML (specifically HIP-NN) provides a pathway to greatly increase the range of feasible simulations while retaining quantum-level accuracy.« less

Authors:
ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [2];  [3]; ORCiD logo [1];  [4];  [5]; ORCiD logo [1]; ORCiD logo [1]
  1. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
  2. Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Univ. of Florida, Gainesville, FL (United States)
  3. Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Univ. of Southern California, Los Angeles, CA (United States)
  4. Univ. of North Carolina, Chapel Hill, NC (United States)
  5. Univ. of Florida, Gainesville, FL (United States)
Publication Date:
Research Org.:
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA); USDOE Laboratory Directed Research and Development (LDRD) Program
OSTI Identifier:
1467336
Report Number(s):
LA-UR-18-22005
Journal ID: ISSN 1549-9618
Grant/Contract Number:  
AC52-06NA25396
Resource Type:
Accepted Manuscript
Journal Name:
Journal of Chemical Theory and Computation
Additional Journal Information:
Journal Volume: 14; Journal Issue: 9; Journal ID: ISSN 1549-9618
Publisher:
American Chemical Society
Country of Publication:
United States
Language:
English
Subject:
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY; 97 MATHEMATICS AND COMPUTING

Citation Formats

Nebgen, Benjamin Tyler, Lubbers, Nicholas Edward, Smith, Justin Steven, Sifain, Andrew E., Lokhov, Andrey, Isayev, Olexandr, Roitberg, Adrian, Barros, Kipton Marcos, and Tretiak, Sergei. Transferable Dynamic Molecular Charge Assignment Using Deep Neural Networks. United States: N. p., 2018. Web. doi:10.1021/acs.jctc.8b00524.
Nebgen, Benjamin Tyler, Lubbers, Nicholas Edward, Smith, Justin Steven, Sifain, Andrew E., Lokhov, Andrey, Isayev, Olexandr, Roitberg, Adrian, Barros, Kipton Marcos, & Tretiak, Sergei. Transferable Dynamic Molecular Charge Assignment Using Deep Neural Networks. United States. doi:10.1021/acs.jctc.8b00524.
Nebgen, Benjamin Tyler, Lubbers, Nicholas Edward, Smith, Justin Steven, Sifain, Andrew E., Lokhov, Andrey, Isayev, Olexandr, Roitberg, Adrian, Barros, Kipton Marcos, and Tretiak, Sergei. Tue . "Transferable Dynamic Molecular Charge Assignment Using Deep Neural Networks". United States. doi:10.1021/acs.jctc.8b00524. https://www.osti.gov/servlets/purl/1467336.
@article{osti_1467336,
title = {Transferable Dynamic Molecular Charge Assignment Using Deep Neural Networks},
author = {Nebgen, Benjamin Tyler and Lubbers, Nicholas Edward and Smith, Justin Steven and Sifain, Andrew E. and Lokhov, Andrey and Isayev, Olexandr and Roitberg, Adrian and Barros, Kipton Marcos and Tretiak, Sergei},
abstractNote = {We report that the ability to accurately and efficiently compute quantum-mechanical partial atomistic charges has many practical applications, such as calculations of IR spectra, analysis of chemical bonding, and classical force field parametrization. Machine learning (ML) techniques provide a possible avenue for the efficient prediction of atomic partial charges. Modern ML advances in the prediction of molecular energies [i.e., the hierarchical interacting particle neural network (HIP-NN)] has provided the necessary model framework and architecture to predict transferable, extensible, and conformationally dynamic atomic partial charges based on reference density functional theory (DFT) simulations. Utilizing HIP-NN, we show that ML charge prediction can be highly accurate over a wide range of molecules (both small and large) across a variety of charge partitioning schemes such as the Hirshfeld, CM5, MSK, and NBO methods. To demonstrate transferability and size extensibility, we compare ML results with reference DFT calculations on the COMP6 benchmark, achieving errors of 0.004e– (elementary charge). This is remarkable since this benchmark contains two proteins that are multiple times larger than the largest molecules in the training set. An application of our atomic charge predictions on nonequilibrium geometries is the generation of IR spectra for organic molecules from dynamical trajectories on a variety of organic molecules, which show good agreement with calculated IR spectra with reference method. Critically, HIP-NN charge predictions are many orders of magnitude faster than direct DFT calculations. Lastly, these combined results provide further evidence that ML (specifically HIP-NN) provides a pathway to greatly increase the range of feasible simulations while retaining quantum-level accuracy.},
doi = {10.1021/acs.jctc.8b00524},
journal = {Journal of Chemical Theory and Computation},
number = 9,
volume = 14,
place = {United States},
year = {2018},
month = {7}
}

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Approaching coupled cluster accuracy with a general-purpose neural network potential through transfer learning
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