Ji Qi: DImensionality-Reduced Encoded Clusters with sTratified (DIRECT) sampling for Robust ... скачать в хорошем качестве

Ji Qi: DImensionality-Reduced Encoded Clusters with sTratified (DIRECT) sampling for Robust ...

DImensionality-Reduced Encoded Clusters with sTratified (DIRECT) sampling for Robust Training of Machine Learning Interatomic Potentials Abstract: Machine learning interatomic potentials (MLIPs) enable accurate simulations of materials at scales beyond conventional first-principles approaches, and they have played increasingly important roles in understanding and design of materials. However, MLIPs are only as accurate and robust as the data they are trained on. In this seminar, I will present DImensionality-Reduced Encoded Clusters with sTratified (DIRECT) sampling as an approach to select a robust training set of structures from a large and complex configuration space. By applying DIRECT sampling on the Materials Project relaxation trajectories dataset with over one million structures and 89 elements, we develop an improved materials 3-body graph network (M3GNet) universal potential that extrapolate more reliably to unseen structures. We further show that molecular dynamics (MD) simulations with universal potentials such as M3GNet can be used in place of expensive ab initio MD to rapidly create a large configuration space for target materials systems. For demonstration, we combined this scheme with DIRECT sampling to develop a reliable moment tensor potential for titanium hydrides without the need for iterative augmentation of training structures.