You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
Copy file name to clipboardExpand all lines: _posts/2025-10-16-jax_refine.md
-2Lines changed: 0 additions & 2 deletions
Display the source diff
Display the rich diff
Original file line number
Diff line number
Diff line change
@@ -1,4 +1,3 @@
1
-
```markdown
2
1
---
3
2
layout: post
4
3
title: "Optimizing Molecular Dynamics Weights with Machine Learning Tools"
@@ -121,5 +120,4 @@ Karson’s implementation, `pearson_target.py`, is available [here]https://githu
121
120
122
121
**TL;DR:**
123
122
By treating MD frame weights as trainable parameters in a differentiable Pearson correlation objective, we can use ML optimizers like Adam to rapidly identify which parts of a trajectory best explain experimental diffuse scattering — turning a brute-force search into a smooth, data-driven optimization problem.
0 commit comments