Bayesian optimization (BO) is a powerful technology for optimizing noisy expensive-to-
evaluate black-box functions, with a broad range of real-world applications in science …

Over the last few years there has been an increasing number of papers using machine
learning (ML) as a tool to aid research directed towards perovskite solar cells. This review …

Flexible thermoelectric generators (TEGs) have shown immense potential for serving as a
power source for wearable electronics and the Internet of Things. A key challenge …

One of the most essential factors in the current study is effectively harvesting the Maximum
Power Extraction (MPE) from the Photovoltaic (PV) panel. The primary difficulties in …

The optimization of the electrodes manufacturing process constitutes a critical step to ensure
high-quality Lithium-Ion Battery (LIB) cells, in particular for automotive applications. Because …

Optimizing material compositions often enhances thermoelectric performances. However,
the large selection of possible base elements and dopants results in a vast composition …

Crystallization process operated in MSMPR mode is of great relevance in manufacturing
because of its low operational requirements and easy maintenance. However, to enhance …

Bayesian Optimization is a useful tool for experiment design. Unfortunately, the classical,
sequential setting of Bayesian Optimization does not translate well into laboratory …

Material science has historically evolved in tandem with advancements in technologies for
characterization, synthesis, and computation. Another type of technology to add to this mix is …

Data-driven materials design often encounters challenges where systems possess
qualitative (categorical) information. Specifically, representing Metal-organic frameworks …