Machine learning and density functional theory

Abstract

Over the past decade machine learning has made significant advances in approximating density functionals, but whether this signals the end of human-designed functionals remains to be seen. Ryan Pederson, Bhupalee Kalita and Kieron Burke discuss the rise of machine learning for functional design.

References in corpus (3)

• By-passing the Kohn-Sham equations with machine learning
• Finding Density Functionals with Machine Learning
• Kohn-Sham equations as regularizer: building prior knowledge into machine-learned physics

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• Learning classical density functionals for ionic fluids
• Why neural functionals suit statistical mechanics
• Physics-informed Bayesian inference of external potentials in classical density-functional theory
• Hyper-density functional theory of soft matter
• Grad DFT: a software library for machine learning enhanced density functional theory
• Neural density functionals: Local learning and pair-correlation matching
• Benchmark Phaseless Auxiliary-Field Quantum Monte Carlo Method for Small Molecules
• Neural density functional theory of liquid-gas phase coexistence
• Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems
• Why hyperdensity functionals describe any equilibrium observable
• Convolutional network learning of self-consistent electron density via grid-projected atomic fingerprints
• Deep learning density functionals for gradient descent optimization
• Nuclear mass predictions based on deep neural network and finite-range droplet model (2012)
• Inverting the Kohn-Sham equations with physics-informed machine learning
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• Determining the chemical potential via universal density functional learning
• Learning Density Functionals from Noisy Quantum Data
• An Active Learning-Based Streaming Pipeline for Reduced Data Training of Structure Finding Models in Neutron Diffractometry
• Efficient Mean-Field Simulation of Quantum Circuits Inspired by Density Functional Theory
• Physics-enhanced neural networks for equation-of-state calculations
• A unified machine-learning framework for ab initio multiscale modeling of liquids
• Electronic structures of crystalline and amorphous GeSe and GeSbTe compounds using machine learning empirical pseudopotentials
• eminus -- Pythonic electronic structure theory
• Machine learning approach for vibronically renormalized electronic band structures
• Neural Polarization: Toward Electron Density for Molecules by Extending Equivariant Networks