Discovery of Solubilizers to Enhance the Swelling Kinetics of Epoxy SU-8 via Virtual Screening
Bibliographic record
Abstract
The slow swelling kinetics of SU-8 epoxy resist hinders its removal in semiconductor fabrication processes. To accelerate solubilizer discovery for swelling, we developed a virtual screening framework integrating molecular dynamics (MD) and machine learning. Using the diffusion coefficient within SU-8 as a key descriptor for solubilizer efficacy, we generated an MD-derived data set to train a graph convolutional neural network (GCN), enabling diffusivity predictions across >20 million compounds. This pipeline identified 4,6-decadienal (4,6-DDA) as a promising candidate. Experimentally, a 10 wt % aqueous solution of 4,6-DDA achieved 80.16% SU-8 swelling within 60 min, which was 3-fold greater than that of propylene carbonate. Simulations confirmed rapid 4,6-DDA penetration and enhanced performance in water, which is consistent with experimental measurements. This work demonstrates a rational simulation-guided strategy for the discovery of functional molecules for industrial processes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".