Gaussian Approximation Potentials: the accuracy of quantum mechanics, without the electrons

2009/10/31 by Albert P. Bartók, Mike C. Payne, Risi Kondor +1 · 16 citations
Physics and Astronomy · #physics.comp-ph #cond-mat.mtrl-sci

paper · pdf · doi:10.1103/physrevlett.104.136403

published as Phys. Rev. Lett. 104 136403, 2010 · v3-4: added new material and references

arxiv created 2009/12/07 · arxiv updated 2015/05/14

Abstract

We introduce a class of interatomic potential models that can be automatically generated from data consisting of the energies and forces experienced by atoms, derived from quantum mechanical calculations. The resulting model does not have a fixed functional form and hence is capable of modeling complex potential energy landscapes. It is systematically improvable with more data. We apply the method to bulk carbon, silicon and germanium and test it by calculating properties of the crystals at high temperatures. Using the interatomic potential to generate the long molecular dynamics trajectories required for such calculations saves orders of magnitude in computational cost.

Cited by

• Economised path integrals
• Machine learning determines the Mg2SiO4 P-T phase diagram
• Spectral neighbor analysis method for automated generation of quantum-accurate interatomic potentials
• Spatial statistics for screening molecular structures
• NEP89: Universal neuroevolution potential for inorganic and organic materials across 89 elements
• Self-consistency error correction for accurate machine learning potentials from variational Monte Carlo
• Data-Driven Thermal and Mechanical Modeling of Defective Covalent Organic Frameworks
• AccelNet: Exact backward-compatible acceleration of polynomial angular descriptors through Cartesian moment factorization
• Scalable Data-Driven Basis Selection for Linear Machine Learning Interatomic Potentials
• Moment Tensor Potential and Equivariant Tensor Network Potential with explicit dispersion interactions
• Improving robustness and training efficiency of machine-learned potentials by incorporating short-range empirical potentials
• Experimental evidence of quantum Drude oscillator behavior in liquids revealed with probabilistic iterative Boltzmann inversion
• Accurate Modeling of LEGO-like vdW Heterostructures: Integrating Machine Learned with Anisotropic Interlayer Potentials
• Gas-solid Reaction Dynamics on Li₆PS₅Cl Surfaces: A Case Study of the Influence of CO₂ and CO₂/O₂ Atmospheres Using AIMD and MLFF Simulations
• Reconstructions and Dynamics of β-Lithium Thiophosphate Surfaces
• Accurate Machine Learning Interatomic Potentials for Polyacene Molecular Crystals: Application to Single Molecule Host-Guest Systems

Related

• Gaussian Approximation Potential: an interatomic potential derived from first principles Quantum Mechanics
• Gaussian Approximation Potentials: theory, software implementation and application examples
• Gaussian Approximation Potentials: a brief tutorial introduction
• The transformative capability of quantum-accurate machine learning interatomic potentials
• Gaussian approximation potentials: A brief tutorial introduction