Atomic Structure Optimization with Machine-Learning Enabled Interpolation between Chemical Elements
Sami Kaappa, Casper Larsen, Karsten W. Jacobsen
Abstract
We introduce a computational method for global optimization of structure and ordering in atomic systems. The method relies on interpolation between chemical elements, which is incorporated in a machine-learning structural fingerprint. The method is based on Bayesian optimization with Gaussian processes and is applied to the global optimization of Au-Cu bulk systems, Cu-Ni surfaces with CO adsorption, and Cu-Ni clusters. The method consistently identifies low-energy structures, which are likely to be the global minima of the energy. For the investigated systems with 23-66 atoms, the number of required energy and force calculations is in the range 3-75.
Topics & Concepts
Maxima and minimaBayesian optimizationInterpolation (computer graphics)Global optimizationAdsorptionGaussianMaterials scienceRange (aeronautics)Energy minimizationComputer scienceEnergy (signal processing)Statistical physicsChemical physicsAlgorithmPhysicsMachine learningArtificial intelligenceChemistryPhysical chemistryMathematicsQuantum mechanicsMathematical analysisComposite materialMotion (physics)Machine Learning in Materials ScienceComputational Drug Discovery MethodsX-ray Diffraction in Crystallography