Name
Title | ||
|---|---|---|
Deep learning and self-consistent field theory: A path towards accelerating polymer phase discovery |
Abstract | ||
|---|---|---|
•A machine learning methodology is proposed to accelerate polymer SCFT simulations.•The deep learner is trained in Sobolev space.•The neural network is designed to be invariant under spatial shifts.•The Sobolev space-trained learners are used to accelerate saddle point finding. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1016/j.jcp.2021.110519 | Journal of Computational Physics |
Keywords | DocType | Volume |
Self-consistent field theory,Machine learning,Sobolev space,Saddle density fields,Global shift-invariance | Journal | 443 |
ISSN | Citations | PageRank |
0021-9991 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xuan Yao | 1 | 4 | 2.12 |
| Kris Delaney | 2 | 0 | 0.34 |
| Hector Ceniceros | 3 | 0 | 0.34 |
| Glenn Fredrickson | 4 | 0 | 0.34 |