Doctoral Thesis: Learning to Model Atoms Across Scales

By: Xiang Fu
Supervisor: Tommi Jaakkola

Details

• Date: Thursday, May 2
• Time: 1:30 pm - 3:00 pm
• Category: Thesis Defense
• Location: Stata Center, 32-370

Additional Location Details:

Abstract: The understanding of atoms and how they interact forms the foundation of modern natural science, as well as material and drug discovery efforts. Computational chemistry methods such as density functional theory and molecular dynamics simulation can offer an unparalleled spatiotemporal resolution for observing microscopic mechanisms and predicting macroscopic phenomena. However, their computational cost limits the applicable domains and scales. This thesis presents machine learning methods for modeling atoms for tasks across different scales. First, we propose machine learning force fields that can decompose molecular interactions into fast and slow components, and then accelerate molecular simulations through multi-scale integration. Second, we propose an end-to-end workflow for learning time-integrated coarse-grained molecular dynamics using multi-scale graph neural networks. Third, we propose diffusion models for periodic material structures and their multi-scale extension to metal-organic frameworks. These machine-learning methods represent a new paradigm in high-throughput scientific discovery and molecular design.