Cambridge University Engineering Department Publications Database · 2019 · 184 citations · 59 references
Natural SciencesChemistryMaterials ScienceComputational ChemistryMachine LearningTheoretical ChemistryAb Initio MethodsActive LearningEnergy MinimizationMaterial ModelingMolecular DynamicsMolecular SimulationAb Initio Molecular DynamicsActive Learning For OptimizationAmorphous OxidesBayesian OptimizationGaussian Approximation PotentialsMachine Learning Potential Energy SurfacesMachine Learning PotentialsMachine-learned Interatomic Potentials
Abstract: We propose an active learning scheme for automatically sampling a minimum number of uncorrelated configurations for fitting the Gaussian Approximation Potential (GAP). Our active learning scheme consists of an unsupervised machine learning (ML) scheme coupled with a Bayesian optimization technique that evaluates the GAP model. We apply this scheme to a Hafnium dioxide (HfO2) dataset generated from a “melt-quench” ab initio molecular dynamics (AIMD) protocol. Our results show that the active learning scheme, with no prior knowledge of the dataset, is able to extract a configuration that reaches the required energy fit tolerance. Further, molecular dynamics (MD) simulations performed using this active learned GAP model on 6144 atom systems of amorphous and liquid state elucidate the structural properties of HfO2 with near ab initio precision and quench rates (i.e., 1.0 K/ps) not accessible via AIMD. The melt and amorphous X-ray structural factors generated from our simulation are in good agreement with experiment. In addition, the calculated diffusion constants are in good agreement with previous ab initio studies.
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Generalized Gradient Approximation Made Simple
John P. Perdew, Kieron Burke, Matthias Ernzerhof · Physical Review Letters · 1996 · 211.5K citations · Full text
Natural Sciences, Chemistry, Physics +16
Self-Consistent Equations Including Exchange and Correlation Effects
W. Kohn, L. J. Sham · Physical Review · 1965 · 63.2K citations · Full text