Event Date and Time
Zachary Ulissi, Carnegie Mellon University
Increasing computational sophistication and resources can enable a larger and more integrated role of theory in the discovery and understanding of new materials. This process has been slower to infiltrate surface science and catalysis than the field of bulk inorganic materials due to additional scientific complexity of modeling the interface. Most catalyst studies start in a data-poor regime where the material of interest is unrelated to previous to studies (new structure, composition etc) or the computational methods are incompatible with previous studies (different exchange-correlation functionals, methods, etc). Efficient methods to quickly define, schedule, and organize necessary simulations are thus important and enable the application of online design of experiments approaches. I will discuss on-going work and software development to enable data science methods in catalysis including open datasets for the community. I will describe applications of our approach to ordered bimetallic alloy catalysts, with applications to several electrochemical catalyst discovery efforts including CO2 reduction, oxygen reduction, and water splitting chemistry. Finally, I will discuss the transition from data-poor to data-rich regimes and our experiences when data-intensive deep-learning methods become more appropriate than simpler models based on chemical intuition.