Rational Design of Donor–Acceptor Compounds based Catalysts for Artificial Photosynthesis and Nonlinear Optics

This research plan is concerned with the theoretical/computational development of new high-performance catalysts for solar water splitting. Artificial photosynthesis is one of the promising approaches to establish a ubiquitous source of renewable energy and, e.g., to provide a basic supply of fuel to rural areas in underdeveloped countries. It could transform the way we harvest and store solar energy and offers great potential for the widespread replacement of the unsustainable conventional fuels used today. Our research utilizes the predictive power of modern computational chemistry to guide and augment experimental efforts. However, departing from traditional paradigms we combine modeling with cutting-edge virtual high-throughput techniques to assess new compound classes of donoracceptor (D-A) compounds (e.g., porphyrin reported in ACS Macro Lett., 2015, 4 (9), pp 926932) on an unprecedented scale. These D-A compounds may also be a good class of second-order nonlinear optics. The resulting Big Data scenario is addressed using analysis, mining, and modeling tools from cheminformatics and machine learning an effort very much in the spirit of as induced by White House Materials Genome Initiative.