Advisor: Dr. Zachary Ulissi
Time: Jan’20 - Dec’20
Source Code: Github Repository
Assisted with development of Amptorch, a machine learning potential package to model atomic interactions using a Behler-Parinello neural network. The package is built on top of PyTorch, Pytorch Geometric, and Skorch to provide users an easy, flexible, and fast framework to train and iterate new models. Evaluated the performance of amptorch on atomic systems present in Propylene Epoxidation with extensive testing of both Online and Offline Active Learning.
