High throughput computational and experimental methods for accelerated electrochemical materials discovery

Abstract

The full integration of sustainable technologies to combat climate change heavily depends on the discovery of cost-competitive, safe, and durable performative materials, specifically for electrochemical systems that can generate energy, store energy, and produce chemicals. Due to the vast exploration space, scientists have adapted high throughput methods, both computational and experimental, for screening, synthesis, and testing to accelerate material discovery. In this review, we analyze such high throughput methodologies reported in the literature that have been applied to electrochemical material discovery. We find that most reported studies utilize computational methods, including density functional theory and machine learning, over experimental methods. Some labs have combined computational and experimental methods to create powerful tools for a closed loop material discovery process through automated setups and machine learning. Either way, over 80% of the publications we reviewed focus on catalytic materials, revealing a shortage in high throughput ionomer, membrane, electrolyte, and substrate material research. Moreover, we find that most material screening criteria do not consider cost, availability, and safety, all of which are crucial properties when assessing the economic feasibility of proposed materials. In addition, we discover that high throughput electrochemical material discovery research is only being conducted in a handful of countries, revealing the global opportunity to collaborate and share resources and data for further acceleration of material discovery. Finally, we acknowledge the development of autonomous labs as the future of high throughput research methodologies.