The highly trained algorithm combed through a defined dataset to accurately predict new structures that could fuel processes in clean energy, chemical and automotive industries. According to Mirkin, what makes this so important is the access to unprecedentedly large, quality datasets because machine learning models and AI algorithms can only be as good as the data used to train them. But the loosely synonymous "materials genome" includes nanoparticle combinations of any of the usable 118 elements in the periodic table, as well as parameters of shape, size, phase morphology, crystal structure and more. Machine learning applications are ideally suited to tackle the complexity of defining and mining the materials genome, but are gated by the ability to create datasets to train algorithms in the space. "As these data suggest, the application of machine learning, combined with Megalibrary technology, may be the path to finally defining the materials genome," said Joseph Montoya, senior research scientist at TRI. Identifying new green catalysts will enable the conversion of waste products and plentiful feedstocks to useful matter, hydrogen generation, carbon dioxide utilization and the development of fuel cells.