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Found 4 papers, 3 papers with code
Active Learning of Molecular Data for Task-Specific Objectives
We established that the performance of AL depends on the relative distribution of the target molecules in comparison to the total dataset distribution, with the largest computational savings achieved when their overlap is minimal.
Learning Relevant Contextual Variables Within Bayesian Optimization
Contextual Bayesian Optimization (CBO) efficiently optimizes black-box functions with respect to design variables, while simultaneously integrating contextual information regarding the environment, such as experimental conditions.
Projective Preferential Bayesian Optimization
This is demonstrated in a user experiment in which the user feedback comes in the form of optimal position and orientation of a molecule adsorbing to a surface.
DScribe: Library of Descriptors for Machine Learning in Materials Science
DScribe is a software package for machine learning that provides popular feature transformations ("descriptors") for atomistic materials simulations.
