43 research outputs found
Dynamic tight binding for large-scale electronic-structure calculations of semiconductors at finite temperatures
Calculating the electronic structure of materials at finite temperatures is
important for rationalizing their physical properties and assessing their
technological capabilities. However, finite-temperature calculations typically
require large system sizes or long simulation times. This is challenging for
non-empirical theoretical methods because the involved bottleneck of performing
many first-principles calculations can pose a steep computational barrier for
larger systems. While machine-learning molecular dynamics enables
large-scale/long-time simulations of the structural properties, the difficulty
of computing in particular the electronic structure of large and disordered
materials still remains. In this work, we suggest an adaptation of the
tight-binding formalism which allows for computationally efficient calculations
of temperature-dependent properties of semiconductors. Our dynamic
tight-binding approach utilizes hybrid-orbital basis functions and a modeling
of the distance dependence of matrix elements via numerical integration of
atomic orbitals. We show that these design choices lead to a dynamic
tight-binding model with a minimal amount of parameters which are
straightforwardly optimized using density functional theory. Combining dynamic
tight-binding with machine learning molecular dynamics and hybrid density
functional theory, we find that it accurately describes finite-temperature
electronic properties in comparison to experiment for the prototypical
semiconductor gallium-arsenide