Research in chemistry increasingly requires interdisciplinary work prompted by, among
other things, advances in computing, machine learning, and artificial intelligence. Everyone …

The design of chemical-based products and functional materials is vital to modern
technologies, yet remains expensive and slow. Artificial intelligence and machine learning …

Molecular optimization is a fundamental goal in the chemical sciences and is of central
interest to drug and material design. In recent years, significant progress has been made in …

Bayesian optimization (BayesOpt) is a gold standard for query-efficient continuous
optimization. However, its adoption for drug design has been hindered by the discrete, high …

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 …

Bayesian optimization over the latent spaces of deep autoencoder models (DAEs) has
recently emerged as a promising new approach for optimizing challenging black-box …

Experimental solid mechanics is at a pivotal point where machine learning (ML) approaches
are rapidly proliferating into the discovery process due to significant advances in data …

We introduce GAUCHE, an open-source library for GAUssian processes in CHEmistry.
Gaussian processes have long been a cornerstone of probabilistic machine learning …

A recent development in Bayesian optimization is the use of local optimization strategies,
which can deliver strong empirical performance on high-dimensional problems compared to …

Learning decompositions of expensive-to-evaluate black-box functions promises to scale
Bayesian optimisation (BO) to high-dimensional problems. However, the success of these …