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Machine Learned Hückel Theory: Interfacing Physics and Deep Neural Networks

Journal Article · · Journal of Chemical Physics
DOI:https://doi.org/10.1063/5.0052857· OSTI ID:1805722
 [1];  [2];  [3];  [2];  [1];  [4];  [2];  [2];  [1];  [2]
  1. Carnegie Mellon Univ., Pittsburgh, PA (United States)
  2. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
  3. Los Alamos National Laboratory
  4. Univ. of Southern California, Los Angeles, CA (United States)
+ Show Author Affiliations
The Hückel Hamiltonian is an incredibly simple tight-binding model known for its ability to capture qualitative physics phenomena arising from electron interactions in molecules and materials. Part of its simplicity arises from using only two types of empirically fit physics-motivated parameters: the first describes the orbital energies on each atom and the second describes electronic interactions and bonding between atoms. By replacing these empirical parameters with machine-learned dynamic values, we vastly increase the accuracy of the extended Hückel model. The dynamic values are generated with a deep neural network, which is trained to reproduce orbital energies and densities derived from density functional theory. The resulting model retains interpretability, while the deep neural network parameterization is smooth and accurate and reproduces insightful features of the original empirical parameterization. Altogether, this work shows the promise of utilizing machine learning to formulate simple, accurate, and dynamically parameterized physics models.
Research Organization:
Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
Sponsoring Organization:
USDOE; USDOE Laboratory Directed Research and Development (LDRD) Program
Grant/Contract Number:
89233218CNA000001
OSTI ID:
1805722
Alternate ID(s):
OSTI ID: 1828727
OSTI ID: 1798538
Report Number(s):
LA-UR--19-29765; LA-UR--21-30589
Journal Information:
Journal of Chemical Physics, Journal Name: Journal of Chemical Physics Journal Issue: 24 Vol. 154; ISSN 0021-9606
Publisher:
American Institute of Physics (AIP)Copyright Statement
Country of Publication:
United States
Language:
English

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37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY
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