Neural-network Density Functional Theory Based on Variational Energy Minimization

Abstract

Deep-learning density functional theory (DFT) shows great promise to significantly accelerate material discovery and potentially revolutionize materials research. However, current research in this field primarily relies on data-driven supervised learning, making the developments of neural networks and DFT isolated from each other. In this work, we present a theoretical framework of neural-network DFT, which unifies the optimization of neural networks with the variational computation of DFT, enabling physics-informed unsupervised learning. Moreover, we develop a differential DFT code incorporated with deep-learning DFT Hamiltonian, and introduce algorithms of automatic differentiation and backpropagation into DFT, demonstrating the capability of neural-network DFT. The physics-informed neural-network architecture not only surpasses conventional approaches in accuracy and efficiency, but also offers a new paradigm for developing deep-learning DFT methods.

6 pages, 4 figures

References in corpus (12)

• The SIESTA method for ab initio order-N materials simulation
• Efficient index handling of multidimensional periodic boundary conditions
• Deep Potential Molecular Dynamics: a scalable model with the accuracy of quantum mechanics
• Solving the Quantum Many-Body Problem with Artificial Neural Networks
• Nearsightedness of Electronic Matter
• Ab-Initio Solution of the Many-Electron Schrödinger Equation with Deep Neural Networks
• Deep-Learning Density Functional Theory Hamiltonian for Efficient ab initio Electronic-Structure Calculation
• General framework for E(3)-equivariant neural network representation of density functional theory Hamiltonian
• Deep-learning electronic-structure calculation of magnetic superstructures
• Deep-learning density functional perturbation theory
• The MD17 Datasets from the Perspective of Datasets for Gas-Phase "Small" Molecule Potentials
• Neural network representation of electronic structure from $ab$ $initio$ molecular dynamics

Cited by in corpus (3)

• AI-driven inverse design of materials: Past, present and future
• Multi-channel machine learning based nonlocal kinetic energy density functional for semiconductors
• Algorithmic differentiation for plane-wave DFT: materials design, error control and learning model parameters