A Holistic Data-Driven Approach to Synthesis Predictions of Colloidal Nanocrystal Shapes
Bibliographic record
Abstract
The ability to precisely design colloidal nanocrystals (NCs) has far-reaching implications in optoelectronics, catalysis, biomedicine, and beyond. Achieving such control is generally based on a trial-and-error approach. Data-driven synthesis holds promise to advance both discovery and mechanistic knowledge. Herein, we contribute to advancing the current state of the art in the chemical synthesis of colloidal NCs by proposing a machine-learning toolbox that operates in a low-data regime, yet comprehensive of the most typical parameters relevant for colloidal NC synthesis. The developed toolbox predicts the NC shape given the reaction conditions and proposes reaction conditions given a target NC shape using Cu NCs as the model system. By classifying NC shapes on a continuous energy scale, we synthesize an unreported shape, which is the Cu rhombic dodecahedron. This holistic approach integrates data-driven and computational tools with materials chemistry. Such development is promising to greatly accelerate materials discovery and mechanistic understanding, thus advancing the field of tailored materials with atomic-scale precision tunability.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".