Designing functional molecules and advanced materials requires complex design choices:
tuning continuous process parameters such as temperatures or flow rates, while …

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

We introduce a method combining variational autoencoders (VAEs) and deep metric
learning to perform Bayesian optimisation (BO) over high-dimensional and structured input …

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

Reaction additives are critical in dictating the outcomes of chemical processes making their
effective screening vital for research. Conventional high-throughput experimentation tools …

Antibodies are canonically Y-shaped multimeric proteins capable of highly specific
molecular recognition. The CDRH3 region located at the tip of variable chains of an antibody …

Bayesian optimisation is a sample-efficient search methodology that holds great promise for
accelerating drug and materials discovery programs. A frequently-overlooked modelling …

The optical and UV variability of the majority of active galactic nuclei may be related to the
reprocessing of rapidly changing X-ray emission from a more compact region near the …

Photoswitchable molecules display two or more isomeric forms that may be accessed using
light. Separating the electronic absorption bands of these isomers is key to selectively …

This paper introduces a modular framework for Mixed-variable and Combinatorial Bayesian
Optimization (MCBO) to address the lack of systematic benchmarking and standardized …