AI-accelerated grand-canonical method for surface processes

Yuanyuan Zhou

Leibniz institute for crystal growth, Berlin, Germany

The processes occurring at surfaces play a critical role in the growth, manufacture and performance of advanced materials, e.g., semiconductor thin film crystal growth. Such systems are controlled by atomistic processes, growth mechanisms, and their coupling involving a wide range of length and time scales that are difficult to probe by experiment alone. The ab initio grand-canonical method we developed enable to characterize the atomistic restructuring of surfaces under realistic conditions including configurational as well as the vibrational free energies. In this talk, I will present the predictive power of the grand-canonical method by taking oxides semiconductor as an example. Furthermore, I will talk about how AI assists grand-canonical method to tackle the complex surface processes at larger length and time scale.