Learning to Model Atoms Across Scales
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...
Main Author: | Fu, Xiang |
---|---|
Other Authors: | Jaakkola, Tommi S. |
Format: | Thesis |
Published: |
Massachusetts Institute of Technology
2024
|
Online Access: | https://hdl.handle.net/1721.1/156328 |
Similar Items
-
Zirconium oxidation on the atomic scale.
by: Hudson, D, et al.
Published: (2009) -
Graph dynamical networks for unsupervised learning of atomic scale dynamics in materials
by: Xie, Tian, et al.
Published: (2021) -
Graph dynamical networks for unsupervised learning of atomic scale dynamics in materials
by: Xie, Tian, et al.
Published: (2022) -
Learning generative models across incomparable spaces
by: Bunne, C, et al.
Published: (2021) -
Learning generative models across incomparable spaces
by: Bunne, Charlotte, et al.
Published: (2022)