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Surrogate-based LJ optimization

Surrogate-based LJ optimization

Driver

Supervisor

Contributors

Stakeholder

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

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

Milestone

Status

Finish preliminary testing for surrogate model building

status:IN PROGRESS

Finish project outline/plan

status:IN PROGRESS

Build naive surrogate and perform optimizations for “pure only” training set (experiments 1/2 in the outline)

status:NOT STARTED

Benchmark optimized parameters for “pure only” training set (experiment 2)

status:NOT STARTED

Build iteratively refined surrogate (experiment 3) and perform optimization

status:NOT STARTED

Build surrogate with Bayesian optimization and perform parameter optimization

status:NOT STARTED

Build surrogate model for “mixture only” training set (experiment 3) and perform parameter optimization

status:NOT STARTED

Benchmark optimized parameters for “mixture only” training set

status:NOT STARTED

Write Draft Manuscript

status:NOT STARTED

Final Manuscript

status:NOT STARTED

Actions

Notes

References

https://docs.google.com/document/d/1fCVAEqKcZFzZID-XyOh06wF1NMNIUQLneK-dKwmb4eA/edit

Surrogate Optimization collection on OpenFF Zotero.

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