Self-driving laboratories (SDLs) promise an accelerated application of the scientific method.
Through the automation of experimental workflows, along with autonomous experimental …

Abstract Machine learning has transformed many fields and has recently found applications
in chemistry and materials science. The small datasets commonly found in chemistry …

Designing and generating new data under targeted properties has been attracting various
critical applications such as molecule design, image editing and speech synthesis …

Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural
sciences. Today, AI has started to advance natural sciences by improving, accelerating, and …

Contemporary materials discovery requires intricate sequences of synthesis, formulation,
and characterization that often span multiple locations with specialized expertise or …

Summary Self-driving laboratories (SDLs), which combine automated experimental
hardware with computational experiment planning, have emerged as powerful tools for …

In the current landscape of deep learning research, there is a predominant emphasis on
achieving high predictive accuracy in supervised tasks involving large image and language …

Sparse Gaussian processes are a key component of high-throughput Bayesian optimisation
(BO) loops; however, we show that existing methods for allocating their inducing points …

Automation is one of the cornerstones of contemporary material discovery. Bayesian
optimization (BO) is an essential part of such workflows, enabling scientists to leverage prior …

Hyperparameter optimization is very frequently employed in machine learning. However, an
optimization of a large space of parameters could result in overfitting of models. In recent …