A5084288950- Advanced Chemical Physics Studies
- Spectroscopy and Quantum Chemical Studies
- Physics of Superconductivity and Magnetism
- Catalysis and Oxidation Reactions
- Machine Learning in Materials Science
- Quantum and electron transport phenomena
- Matrix Theory and Algorithms
- Microstructure and mechanical properties
- Surface and Thin Film Phenomena
- Organic and Molecular Conductors Research
- Quantum, superfluid, helium dynamics
- Metallurgy and Material Forming
- Ultrasonics and Acoustic Wave Propagation
- Thermodynamic and Structural Properties of Metals and Alloys
- GaN-based semiconductor devices and materials
- Theoretical and Computational Physics
- Electrocatalysts for Energy Conversion
- Aluminum Alloy Microstructure Properties
- Spectroscopy and Laser Applications
- Magnetic properties of thin films
- Electrochemical Analysis and Applications
University of Illinois Urbana-Champaign
2023-2024
University of Tennessee at Knoxville
2020
Los Alamos National Laboratory
2018-2019
We describe a new open-source Python-based package for high accuracy correlated electron calculations using quantum Monte Carlo (QMC) in real space: PyQMC. PyQMC implements modern versions of QMC algorithms an accessible format, enabling algorithmic development and easy implementation complex workflows. Tight integration with the PySCF environment allows simple comparison between other many-body wave function techniques, as well access to trial functions.
Determining the range of validity Migdal's approximation for electron-phonon ($e$-ph) coupled systems is a long-standing problem. Many attempts to answer this question employ Holstein Hamiltonian, where electron density couples linearly local lattice displacements. When these displacements are large, however, nonlinear corrections interaction must also be included, which can significantly alter physical picture obtained from model. Using determinant quantum Monte Carlo and self-consistent...
In this work, we demonstrate how extended Lagrangian Born-Oppenheimer quantum-based molecular dynamics (XL-BOMD) can be used to simulate heterogeneous electrocatalytic reactions. particular, apply our framework study the oxygen reduction reaction (ORR) mechanism on nitrogen-doped graphene in an aqueous solution. The electronic ground state and total energy of XL-BOMD are stabilized through nuclear equations motion assisted by integral kernel updated with low-rank approximations. A species...
Abstract Variational Monte Carlo methods have recently been applied to the calculation of excited states; however, it is still an open question what objective function most effective. A promising approach optimize states using a penalty minimize overlap with lower eigenstates, which has drawback that must be computed one at time. We derive general framework for constructing functions minima lowest N eigenstates many-body Hamiltonian. The uses weighted average energies and penalty, satisfy...
Variational Monte Carlo methods have recently been applied to the calculation of excited states; however, it is still an open question what objective function most effective. A promising approach optimize states using a penalty minimize overlap with lower eigenstates, which has drawback that must be computed one at time. We derive general framework for constructing functions minima lowest $N$ eigenstates many-body Hamiltonian. The uses weighted average energies and penalty, satisfy several...
We demonstrate the applicability of extended Lagrangian Born-Oppenheimer quantum-based molecular dynamics (XL-BOMD) to model electron transfer reactions occurring on solid-liquid interfaces. Specifically, we consider reduction O$_2$ as catalyzed at interface an N-doped graphene sheet and H$_2$O fuel cell cathodes. This system is a good testbed for next-generation computational chemistry methods since electrochemical functionalities strongly depend atomic-scale quantum mechanics. As opposed...
We describe a new open-source Python-based package for high accuracy correlated electron calculations using quantum Monte Carlo (QMC) in real space: PyQMC. PyQMC implements modern versions of QMC algorithms an accessible format, enabling algorithmic development and easy implementation complex workflows. Tight integration with the PySCF environment allows simple comparison between other many-body wave function techniques, as well access to trial functions.