Progress of Machine Learning in Molecular Crystal Design and Crystallization Development
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 (ML) can optimize the research paradigm and shorten the time from discovery to application of novel functional materials, pharmaceuticals, and fine chemicals. Besides supporting material and drug design, ML is a potentially valuable tool for predictive modeling and process optimization. Herein, we first review the recent progress in data-driven ML for molecular crystal design, including property and structure predictions. ML can accelerate the development of the solvates, co-crystals, and colloidal nanocrystals, and improve the efficiency of crystal design. Next, this review summarizes ML algorithms for crystallization behavior prediction and process regulation. ML models support drug solubility prediction, particle agglomeration prediction, and spherical crystal design. ML-based in situ image processing can extract particle information and recognize crystal products. The application scenarios of ML algorithms utilized in crystallization processes and two control strategies based on supersaturation regulation and image processing are also presented. Finally, emerging techniques and the outlook of ML in drug molecular design and industrial crystallization processes are outlined.
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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 | 0.000 | 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 it