FURI | Spring 2020
Machine Learning and Dimensionality Reduction to Accelerate DFT Simulations of Material Properties and Enable Computational Material Design
This research is focused on applying machine learning to create an alternative to computationally expensive DFT and MD simulations of material properties. We specifically investigate the utility of graph neural network in learning Koopman operators for converting nonlinear (atomic) dynamical models into (approximately) linear ones. For n-body dynamics (MD and DFT), we hypothesize that GNNs will help learn Koopman operators from small scale systems where high-fidelity simulations are affordable and generalize the operators to high cost large scale systems. If successful, the learned GNN model will drastically accelerate material property prediction and enable computational material design.
Hometown: Albuquerque, New Mexico, United States
Graduation date: Spring 2021