Correlative characterization and data science in functional materials

Abstract

The physical sciences community is increasingly taking advantage of the possibilities offered by modern data science to solve problems in experimental chemistry and potentially to change the way we design, conduct and understand results from experiments. Successfully exploiting these opportunities involves considerable challenges. Here, we will present a perspective on the importance of data science and automated approaches in energy materials. We will focus on experimental co-design and its importance to experimental chemistry. We provide examples of how data science is changing the way we conduct experiments, and we outline opportunities for further integration of data science and experimental chemistry to advance these fields. We will specifically provide examples related to generation of solar fuels devices via artificial photosynthesis.