Machine learning for the experimental and computational development of heterogeneous catalysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Machine learning techniques have emerged as a useful tool for identifying complex patterns and correlations in large datasets. These techniques could be particularly useful in heterogeneous catalysis research for enabling the correlation of the catalyst performance to its physicochemical properties. So far in the chemistry and material science communities, machine learning models have mostly been built on high-throughput quantum chemistry calculations, and only selected case studies have led to the experimental discovery of improved catalyst materials. The slow pace and limited number of scientific breakthroughs may be attributed to simplistic assumptions about catalyst structure in quantum chemistry calculations and the incomplete experimental data available. Therefore, we believe that the development of high-throughput approaches closely coupled with machine-learning-based approaches could help accelerate experimental catalysis research. To aid the community, we bring together the available body of work applying high-throughput approaches and machine learning to the development of solid heterogeneous catalysis. We offer an objective view of the trends in the field by performing a detailed and systematic comparison of papers based on the (1) the ML method, the features used as model input and output, (3) the material, device or reaction investigated, (4) the dataset size, and (5) the overall achievement. Furthermore, for models reporting unitless R2 values, we quantitatively analyze the model performance as a function of the features used, the reaction type and the dataset size.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 it