High-Throughput Computational Studies in Catalysis and Materials Research, and Their Impact on Rational Design

Abstract

In the 21st century, many technology fields have become reliant on advancements in process automation. We have seen dramatic growth in areas and industries that have successfully implemented a high level of automation. In drug discovery, for example, it has alleviated an otherwise extremely complex and tedious process and has resulted in the development of several new drugs. Over the last decade, these automation techniques have begun being adapted in the chemical and materials community as well with the goal of exploring chemical space and pursuing the discovery and design of novel compounds for various applications. The impact of new materials on industrial and economic development has been stimulating tremendous research efforts by the materials community, and embracing automation as well as tools from computational and data science have led to acceleration and streamlining of the discovery process. In particular, virtual high-throughput screening (HTPS) is now becoming a mainstream technique to search for materials with properties that are tailored for specific applications. Its efficiency combined with the increasing availability of open-source codes and large computational resources makes it a powerful and attractive tool in materials research. Herein, we will review a selection of recent, high-profile HTPS projects for new materials and catalysts. In the case of catalysts, we focus on the HTPS studies for oxygen reduction reaction, oxygen evolution reaction, hydrogen evolution reaction, and carbon dioxide reduction reaction. Whereas, for other materials applications, we emphasize on the HTPS studies for photovoltaics, gas separation, high-refractive-index materials, and OLEDs.