Surrogate-based LJ optimization
Driver | Supervisor | Contributors | Stakeholder |
|---|---|---|---|
@Owen Madin | @Michael Shirts |
|
|
Objective | Demonstrate the feasibility of surrogate-based optimization for LJ parameters |
Due date | 03/01/2022 |
Status | IN PROGESS |
Problem Statement
We would like to perform better LJ optimizations by building multi-surrogate models that approximate the response of physical property simulated from a force field, as a function of force field parameters
Milestones
Milestone | Status |
|---|---|
Finish preliminary testing for surrogate model building | IN PROGRESS |
Finish project outline/plan | IN PROGRESS |
Build naive surrogate and perform optimizations for “pure only” training set (experiments 1/2 in the outline) | NOT STARTED |
Benchmark optimized parameters for “pure only” training set (experiment 2) | NOT STARTED |
Build iteratively refined surrogate (experiment 3) and perform optimization | NOT STARTED |
Build surrogate with Bayesian optimization and perform parameter optimization | NOT STARTED |
Build surrogate model for “mixture only” training set (experiment 3) and perform parameter optimization | NOT STARTED |
Benchmark optimized parameters for “mixture only” training set | NOT STARTED |
Write Draft Manuscript | NOT STARTED |
Final Manuscript | NOT STARTED |
Actions
Notes
References
https://docs.google.com/document/d/1fCVAEqKcZFzZID-XyOh06wF1NMNIUQLneK-dKwmb4eA/edit
Surrogate Optimization collection on OpenFF Zotero.