High‐Throughput Photocatalysis for Generating Reliable Datasets Analyzed by Machine Learning
Bibliographic record
Abstract
Photocatalysis is an environmentally conscious tool for removing contaminants from water. Novel photocatalytic materials are often measured on ability to degrade a small number of analytes, which may not be indicative of broader applicability. In this work, an experimental method dubbed high-throughput photocatalysis (HTP) is introduced to assay photocatalytic materials against a range of analytes in a time effective manner. HTP is modular; experimental parameters, including matrix, can be changed to fit a proposed application. The photodegradation of each analyte is attained in a consistent manner such that machine learning (ML) models can be applied to the obtained datasets. Three out of the box ML models-linear regression, random forest (RF), and neural network (NN)-are tasked with estimating the percentage removal as a function of irradiation time and molecular structure, as represented by Morgan fingerprints. Leave-out sets demonstrated that RF and NN models did not overfit the training data and reasonably estimated the degradation of unknown molecules. SHapley additive exPlanations values are utilized to correlate molecular substructures to the parent molecule's susceptibility to photocatalytic degradation. These correlations are used to generate heatmaps of estimated reactivity within molecules that corroborate reports in which dye degradation pathways were studied in detail.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".