Computational chemistry and materials science algorithms are now powerful enough that they can predict many properties of materials and molecules before they are synthesized. By implementing and developing new approaches to calculate materials and chemical properties in supercomputers, we have predicted over 100,000 materials for energy storage and catalysis [1-10]. The computations predicted several new materials which were made and tested in the lab [7-10]. The creation of our large amount of materials in-silico, has prompted to create our own type of materials genomes or materials Atlas for different purposes. We have implemented different machine learning methods using these databases to find further materials design principles. Some of the applications of the design principles of materials has been used towards developing an alternative way to generate and store energy; specifically artificial photosynthesis, i.e. conversion of sunlight into chemical fuels and other ways to storage energy [11-17].