Physical Review Letters · 2010 · 3.1K citations · 20 references
Natural SciencesChemistryPhysicsComputational ChemistryComputational PhysicsTheoretical ChemistryAb Initio MethodsApproximation MethodsAtomistic ModelingInteratomic PotentialsMolecular DynamicsMolecular SimulationAb Initio Molecular DynamicsAccuracy-computational Cost TradeoffGaussian Approximation PotentialsInteratomic Potential ModelsMolecular Potential ModelsQuantum Chemical ModelingQuantum Mechanical ModelQuantum Mechanics Modeling
We introduce a class of interatomic potential models that can be automatically generated from quantum‑mechanical data of atomic energies and forces. The models have no fixed functional form, can capture complex energy landscapes, are systematically improvable with additional data, and are applied to bulk crystals to compute high‑temperature properties. Using these potentials to run long molecular dynamics trajectories reduces computational cost by orders of magnitude.
We introduce a class of interatomic potential models that can be automatically generated from data consisting of the energies and forces experienced by atoms, as derived from quantum mechanical calculations. The models do not have a fixed functional form and hence are capable of modeling complex potential energy landscapes. They are systematically improvable with more data. We apply the method to bulk crystals, and test it by calculating properties 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.
20
First principles methods using CASTEP
Stewart J. Clark, Matthew Segall, Chris J. Pickard et al. · Zeitschrift für Kristallographie - Crystalline Materials · 2005 · 14.4K citations · Full text
Natural Sciences, Chemistry, Physics +17
Information theory, inference, and learning algorithms
Choice Reviews Online · 2004 · 6.6K citations
Information Theory, Inference, and Learning Algorithms
Kybernetes · 2004 · 5.1K citations