Machine learning the electronic structure of matter across temperatures

Abstract

We introduce machine learning (ML) models that predict the electronic structure of materials across a wide temperature range. Our models employ neural networks and are trained on density functional theory (DFT) data. Unlike most other ML models that use DFT data, our models directly predict the local density of states (LDOS) of the electronic structure. This provides several advantages, including access to multiple observables such as the electronic density and electronic total free energy. Moreover, our models account for both the electronic and ionic temperatures independently, making them ideal for applications like laser-heating of matter. We validate the efficacy of our LDOS-based models on a metallic test system. They accurately capture energetic effects induced by variations in ionic and electronic temperatures over a broad temperature range, even when trained on a subset of these temperatures. These findings open up exciting opportunities for investigating the electronic structure of materials under both ambient and extreme conditions.

References in corpus (47)

• Quantum ESPRESSO: a modular and open-source software project for quantum simulations of materials
• Advanced capabilities for materials modelling with Quantum ESPRESSO
• Quantum ESPRESSO toward the exascale
• A Spectral Analysis Method for Automated Generation of Quantum-Accurate Interatomic Potentials
• A Performance and Cost Assessment of Machine Learning Interatomic Potentials
• By-passing the Kohn-Sham equations with machine learning
• Machine-learning based interatomic potential for amorphous carbon
• Nearsightedness of Electronic Matter
• Colloquium: Nonlinear collective interactions in quantum plasmas with degenerate electron fluids
• Deep Neural Networks for Accurate Predictions of Garnet Stability
• Path Integral Monte Carlo Simulation of the Warm-Dense Homogeneous Electron Gas
• A Massive Core in Jupiter Predicted From First-Principles Simulations
• Local Spin-density Approximation Exchange-correlation Free-energy Functional
• FPEOS: A First-Principles Equation of State Table of Deuterium for Inertial Confinement Fusion Applications
• Ab initio Exchange-Correlation Free Energy of the Uniform Electron Gas at Warm Dense Matter Conditions
• Accurate molecular polarizabilities with coupled-cluster theory and machine learning
• The first principles calculation of transport coefficients
• Thermal evolution and structure models of the transiting super-Earth GJ 1214b
• Ab initio simulation of warm dense matter
• {\em Ab initio} Quantum Monte Carlo simulation of the warm dense electron gas in the thermodynamic limit
• Learning the electronic density of states in condensed matter
• A Deep Dive into Machine Learning Density Functional Theory for Materials Science and Chemistry
• Nonempirical Semi-local Free-Energy Density Functional for Matter Under Extreme Conditions
• Exact conditions and scaling relations in finite temperature density functional theory
• Quantum deep field: data-driven wave function, electron density generation, and atomization energy prediction and extrapolation with machine learning
• Uniform electron gas at finite temperatures
• Electron-ion scattering in dense multi-component plasmas: application to the outer crust of an accreting neutron star
• Data-driven Material Models for Atomistic Simulation
• Electronic effects in high-energy radiation damage in iron
• Accelerating Finite-temperature Kohn-Sham Density Functional Theory with Deep Neural Networks
• Gradient corrections to the exchange-correlation free energy
• Extensive deep neural networks for transferring small scale learning to large scale systems
• Exact conditions on the temperature dependence of density functionals
• Three-Particle Correlations in Simple Liquids
• Exact thermal density functional theory for a model system: Correlation components and accuracy of the zero-temperature exchange-correlation approximation
• The Relevance of Electronic Perturbations in the Warm Dense Electron Gas
• Benchmarking Exchange-Correlation Functionals in the Spin-Polarized Inhomogeneous Electron Gas under Warm Dense Conditions
• Non-empirical mixing coefficient for hybrid XC functionals from analysis of the XC kernel
• Finite-temperature materials modeling from the quantum nuclei to the hot electrons regime
• Connection formulas for thermal density functional theory
• Accelerating Equilibration in First-Principles Molecular Dynamics with Orbital-Free Density Functional Theory
• Assessing the accuracy of hybrid exchange-correlation functionals for the density response of warm dense electrons
• Machine Learned Interatomic Potential for Dispersion Strengthened Plasma Facing Components
• Efficacy of the Radial Pair Potential Approximation for Molecular Dynamics Simulations of Dense Plasmas
• Predicting hot-electron free energies from ground-state data
• Finite Temperature Scaling in Density Functional Theory
• Training-free hyperparameter optimization of neural networks for electronic structures in matter