| --- |
| library_name: pytorch |
| tags: |
| - polymers |
| - molecular-editing |
| - polybert |
| - supervised-learning |
| --- |
| |
| # PolyEdit DFT-consistent task augmentation |
|
|
| This repository contains the SFT policies used for the 369, 1,000, and |
| 2,000-request PolyEdit task-augmentation comparison. Each condition has five |
| seed directories containing the policy, a self-contained policy bundle, the |
| corresponding fine-tuned polyBERT `Egc` verifier, and the run configuration. |
|
|
| ## Training setup |
|
|
| - Property: DFT-labeled chain band gap, `Egc` |
| - Requests: 369 original, then 1,000 and 2,000 after train-only augmentation |
| - Decision steps: 1,263, 5,145, and 11,133 |
| - Edit budget: at most three matched-pair transitions |
| - Policy loss: set-valued negative log-likelihood over valid next edits |
| - Seeds: 1004, 1005, 1006, 1007, 1008 |
| - Evaluation: fixed held-out sources, 112 single-step and 36 multi-step requests |
|
|
| ## Results |
|
|
| Five-seed means and 95% t confidence intervals: |
|
|
| | Requests | Single-target success | Multi-step success | |
| |---:|---:|---:| |
| | 369 | 0.889 ± 0.033 | 0.811 ± 0.089 | |
| | 2,000 | 0.941 ± 0.022 | 0.928 ± 0.019 | |
|
|
| Paired improvements were +0.052 for single-target success (`p=0.040`) and |
| +0.117 for multi-step success (`p=0.025`). |
|
|
| ## Limitations |
|
|
| The policies were trained and evaluated only for `Egc` within the frozen |
| matched-pair graph. Success therefore measures navigation among polymers with |
| available DFT labels. It does not establish performance on arbitrary polymer |
| structures or other properties. |
|
|
