Bayesian optimization is an approach to optimizing objective functions that take a long time
(minutes or hours) to evaluate. It is best-suited for optimization over continuous domains of …

The ever-increasing demand for novel materials with superior properties inspires retrofitting
traditional research paradigms in the era of artificial intelligence and automation. An …

The optimal synthesis of advanced nanomaterials with numerous reaction parameters,
stages, and routes, poses one of the most complex challenges of modern colloidal science …

Bayesian optimization is an approach to optimizing objective functions that take a long time
(minutes or hours) to evaluate. It is best suited for optimization over continuous domains of …

Big Data applications are typically associated with systems involving large numbers of
users, massive complex software systems, and large-scale heterogeneous computing and …

Automatic Chemical Design is a framework for generating novel molecules with optimized
properties. The original scheme, featuring Bayesian optimization over the latent space of a …

Machine learning is an established and frequently used technique in industry and
academia, but a standard process model to improve success and efficiency of machine …

Bayesian optimization is an effective methodology for the global optimization of functions
with expensive evaluations. It relies on querying a distribution over functions defined by a …

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

Machine learning combines techniques from statistics and artificial intelligence to create
algorithms that can learn from empirical data and generalize to solve problems in various …