As materials researchers increasingly embrace machine-learning (ML) methods, it is natural
to wonder what lessons can be learned from other fields undergoing similar developments …

Bayesian optimization has emerged at the forefront of expensive black-box optimization due
to its data efficiency. Recent years have witnessed a proliferation of studies on the …

Recent interest in complex and computationally expensive machine learning models with
many hyperparameters, such as automated machine learning (AutoML) frameworks and …

Bayesian optimization has recently emerged as a popular method for the sample-efficient
optimization of expensive black-box functions. However, the application to high-dimensional …

Structure-based virtual screening is an important tool in early stage drug discovery that
scores the interactions between a target protein and candidate ligands. As virtual libraries …

Develo** a scalable manufacturing technique for perovskite solar cells requires process
optimization in high-dimensional parameter space. Herein, we present a machine learning …

Knowledge transfer plays a vastly important role in solving multitask optimization problems
(MTOPs). Many existing methods transfer task-specific knowledge, such as the high-quality …

Many real world scientific and industrial applications require optimizing multiple competing
black-box objectives. When the objectives are expensive-to-evaluate, multi-objective …

We report Phoenics, a probabilistic global optimization algorithm identifying the set of
conditions of an experimental or computational procedure which satisfies desired targets …

Bayesian Optimization (BO), the application of Bayesian function approximation to finding
optima of expensive functions, has exploded in popularity in recent years. In particular, much …