| 초록 |
Electochemical activation of small molecules (CO2, N2, etc) towards value-added products is an efficient and sustainable way to address energy problems and global climate changes. In this talk, I will discuss some of our recent efforts to understand and design new materials towards electrochemical catalysis using density functional calculations. In addition, solid state materials are often complex, but can also be potentially highly tunable if there is a way to accurately extrapolate the large set of existing data for a new discovery, an area in which machine learning can help significantly accelerate the discovery proces with improved accuracy. At the end of this talk, thus, I will briefly describe some of our initial efforts to use machine learning for chemical science that can contribute greatly to creating solutions to catalysis and materials problems, in general. Keywords: Electrochemical catalysis, machine learning, density functional calculations |
