Locally coupled open subsystems: A formalism for affordable electronic structure calculations featuring fractional charges and size consistency

Abstract

This manuscript introduces a methodology (within the Born-Oppenheimer picture) to compute electronic ground-state properties of molecules and solids/surfaces with fractionally occupied components. Given a user-defined division of the molecule into subsystems, our theory uses an auxiliary global Hamiltonian that is defined as the sum of subsystem Hamiltonians, plus the spatial integral of a second-quantized local operator that allows the electrons to be transferred between subsystems. This electron transfer operator depends on a local potential that can be determined using density functional approximations and/or other techniques such as machine learning. The present framework employs superpositions of tensor-product wave functions, which can satisfy size consistency and avoid spurious fractional charges at large bond distances. The electronic population of each subsystem is in general a positive real number and is obtained from wave-function amplitudes, which are calculated by means of ground-state matrix diagonalization (or matrix propagation in the time-dependent case). Our method can provide pathways to explore charge-transfer effects in environments where dividing the molecule into subsystems is convenient and to develop computationally affordable electronic structure algorithms.

Document Details

Document Type
Pub Defense Publication
Publication Date
Jul 19, 2018
Source ID
10.1063/1.5038557

Entities

People

  • George C. Schatz
  • Mark Ratner
  • Martín A Mosquera

Organizations

  • Air Force Office of Scientific Research
  • Northwestern University
  • United States Department of Energy

Tags

Fields of Study

  • Physics

Readers

  • Finite Element Method (FEM) for solving Partial Differential Equations (PDEs)
  • Quantum spin resonance or Electron Paramagnetic Resonance spectroscopy.
  • Software Engineering

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms
  • Microelectronics