Machine Learning Applications
The rational design of new materials for optoelectronic applications benefits greatly from an initial computational screening of a large number of candidates using machine learning. In our group, we have combined the efforts of material computationalists and synthetic chemists, to identify the best material candidates for optoelectronic devices. Following this strategy, we have successfully extended the breadth of metal halide perovskite applications beyond the visible spectral region with the discovery of new ultraviolet emitting perovskites.
Miao, Kevin, and colleagues (UofT and CMU) publish “Accelerated discovery of CO2 electrocatalysts using active machine learning” in Nature.
Miao, Kevin, and colleagues (UofT and CMU) publish “Accelerated discovery of CO2 electrocatalysts using active machine learning” in Nature.
