Machine learning-guided design of materials for low-temperature catalysis

Machine learning-guided design of materials for low-temperature catalysis
The design of a material that can catalyze the conversion of CO2 into fuels or chemicals could help us tackle climate change and our dependence on fossil fuels. The project will contribute to this goal by using theoretical methods based on machine learning to direct the search for a material that can hydrogenate CO2 to methanol at low temperatures. The grant will fund the recipient, one postdoc, two PhD students and computational resources.