Machine learning for accuracy in density functional approximations
arXiv:2311.00196 · doi:10.1002/jcc.27366
Abstract
Machine learning techniques have found their way into computational chemistry as indispensable tools to accelerate atomistic simulations and materials design. In addition, machine learning approaches hold the potential to boost the predictive power of computationally efficient electronic structure methods, such as density functional theory, to chemical accuracy and to correct for fundamental errors in density functional approaches. Here, recent progress in applying machine learning to improve the accuracy of density functional and related approximations is reviewed. Promises and challenges in devising machine learning models transferable between different chemistries and materials classes are discussed with the help of examples applying promising models to systems far outside their training sets.
References in corpus (18)
- Quantum ESPRESSO: a modular and open-source software project for quantum simulations of materials
- Restoring the density-gradient expansion for exchange in solids and surfaces
- Generalized gradient approximation for solids and their surfaces
- ANI-1: An extensible neural network potential with DFT accuracy at force field computational cost
- A General-Purpose Machine Learning Framework for Predicting Properties of Inorganic Materials
- Nonlocal van der Waals density functional: The simpler the better
- Localization and delocalization errors in density functional theory and implications for band-gap prediction
- W4 theory for computational thermochemistry: in pursuit of confident sub-kJ/mol predictions
- Efficient and Accurate Machine-Learning Interpolation of Atomic Energies in Compositions with Many Species
- Basis set convergence of post-CCSD contributions to molecular atomization energies
- A Universal Density Matrix Functional from Molecular Orbital-Based Machine Learning: Transferability across Organic Molecules
- Gedanken Densities and Exact Constraints in Density Functional Theory
- Design and Analysis of Machine Learning Exchange-Correlation Functionals via Rotationally Invariant Convolutional Descriptors
- Ground state energy functional with Hartree-Fock efficiency and chemical accuracy
- Predictive Power of the Exact Constraints and Appropriate Norms in Density Functional Theory
- Analysis of over-magnetization of elemental transition metal solids from the SCAN Density Functional
- Machine learning density functionals from the random-phase approximation
- Data-driven and constrained optimization of semi-local exchange and non-local correlation functionals for materials and surface chemistry