Developing a GPU/CPU Gaussian Process Regression code for molecular properties

In this talk, we present lessons learned during the development of a proxy app representative for workloads in producing machine learning interatomic potentials and its application to predict energies and forces of molecules and materials, particularly using Gaussian Approximation Potentials (GAP). We compared the performance of multiple providers of smooth overlap of atomic positions (SOAP) descriptor, in terms of accuracy and speed. We also propose a new SOAP implementation that could work in hybrid GPU/CPU architectures. We trained the potentials with the TensorFlow back-end. We discuss the implications of optimizing hyperparameters of Gaussian Processes.