The solutions to many of society’s most pressing problems rely on the discovery of materials with unprecedented physical and chemical properties that are tailored to an application of interest. Typically, it is not a matter of incremental improvements over existing technologies; rather, there is often an urgent need to identify new kinds of materials altogether. In this talk, I will discuss how quantum chemistry, high-throughput computing, and machine learning can help guide the discovery of novel, energy-relevant materials. I will highlight several representative success stories for this approach as well as potential shortcomings, using the areas of chemical separations and catalysis as demonstrative topics. I will also briefly highlight the open-source software and community science efforts I have contributed to that enables us to leverage the full capabilities of modern high-performance computing resources for materials discovery problems.