Machine learned Hückel theory: Interfacing physics and deep neural networks
Abstract
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. Overall, this work shows the promise of utilizing machine learning to formulate simple, accurate, and dynamically parameterized physics models.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Jun 25, 2021
- Source ID
- 10.1063/5.0052857
Entities
People
- Ben Nebgen
- Christopher Koh
- Guoqing Zhou
- Justin S Smith
- Kipton Barros
- Nicholas Lubbers
- Olexandr Isayev
- Roman I. Zubatyuk
- Sergei Tretiak
- Tetiana A Zubatiuk
Organizations
- Carnegie Mellon University
- Center for Integrated Nanotechnologies
- Los Alamos National Laboratory
- National Science Foundation
- Office of Naval Research Global
- University of Southern California