Ab initio Canonical Sampling based on Variational Inference
arXiv:2210.11531 · doi:10.1103/PhysRevB.106.L161110
Abstract
Finite temperature calculations, based on ab initio molecular dynamics (AIMD) simulations, are a powerful tool able to predict material properties that cannot be deduced from ground state calculations. However, the high computational cost of AIMD limits its applicability for large or complex systems. To circumvent this limitation we introduce a new method named Machine Learning Assisted Canonical Sampling (MLACS), which accelerates the sampling of the Born--Oppenheimer potential surface in the canonical ensemble. Based on a self-consistent variational procedure, the method iteratively trains a Machine Learning Interatomic Potential to generate configurations that approximate the canonical distribution of positions associated with the ab initio potential energy. By proving the reliability of the method on anharmonic systems, we show that the method is able to reproduce the results of AIMD with an ab initio accuracy at a fraction of its computational cost.
References in corpus (25)
- Variational Inference: A Review for Statisticians
- Gaussian Approximation Potentials: the accuracy of quantum mechanics, without the electrons
- Deep Potential Molecular Dynamics: a scalable model with the accuracy of quantum mechanics
- Statistically optimal analysis of samples from multiple equilibrium states
- A Spectral Analysis Method for Automated Generation of Quantum-Accurate Interatomic Potentials
- A Performance and Cost Assessment of Machine Learning Interatomic Potentials
- Comparing molecules and solids across structural and alchemical space
- Lattice dynamics of anharmonic solids from first principles
- Active learning of linearly parametrized interatomic potentials
- Machine-learning based interatomic potential for amorphous carbon
- Active Learning of Uniformly Accurate Inter-atomic Potentials for Materials Simulation
- Achieving DFT accuracy with a machine-learning interatomic potential: thermomechanics and defects in bcc ferromagnetic iron
- The Stochastic Self-Consistent Harmonic Approximation: Calculating Vibrational Properties of Materials with Full Quantum and Anharmonic Effects
- Extending the Accuracy of the SNAP Interatomic Potential Form
- Nonequilibrium free-energy calculation of solids using LAMMPS
- Second order structural phase transitions, free energy curvature, and temperature-dependent anharmonic phonons in the self-consistent harmonic approximation: theory and stochastic implementation
- Large scale ab initio calculations based on three levels of parallelization
- First-Principles Lattice Dynamics Method for Strongly Anharmonic Crystals
- Intrinsic localized mode and low thermal conductivity of PbSe
- Pressure and stress tensor of complex anharmonic crystals within the stochastic self-consistent harmonic approximation
- A nuclear quantum effect with pure anharmonicity and the anomalous thermal expansion of silicon
- Anharmonicity Measure for Materials
- Explicit Multi-element Extension of the Spectral Neighbor Analysis Potential for Chemically Complex Systems
- Quantum Self-Consistent Ab-Initio Lattice Dynamics
- - phase transition of zirconium predicted by on-the-fly machine-learned force field