Deep Learning for molecular simulation and spectra calculation

I will discuss some mathematical perspectives of model representation and exploration of ab initio data for generating reliable deep learning-based models that represent the interatomic potential energy surface and electronic information of complex systems. This gives us an unprecedented opportunity to perform large-scale molecular simulation and extract direct experimental observables, such as the infrared and Raman spectra. I will show how these methodologies help us understand the complex nature of water in a large region of its thermodynamic phase diagram.