ArticleJournal of the American Chemical Society2025
A Holistic Data-Driven Approach to Synthesis Predictions of Colloidal Nanocrystal Shapes.
Article in Journal of the American Chemical Society, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- AI-driven electrocatalysis: reinforcement learning-based control and theoretical modeling of the glycerol oxidation reaction.Energy & environmental science · 2026Article
- Machine Learning-Driven Nanoscale Synthesis for Electrocatalytic Performance: From Data-Driven Methodologies to Closed-Loop Optimization.Advanced materials (Deerfield Beach, Fla.) · 2026Review
- Machine Learning Optimization of Laser Ablation in Liquid for the Green and Low-Cost Synthesis of Clean Gold Nanoparticles.Journal of the American Chemical Society · 2026Article
- Large language models as uncertainty-calibrated optimizers for experimental discovery.Nature machine intelligence · 2026Article
- Emerging Technologies and Integrated Interdisciplinary Strategies for Mitigating Protein Aggregation in Therapeutic Formulations.Pharmaceutical research · 2026Review
- Electrochemical Fingerprints of Nanostructured Cu and Ag Electrodes.ACS electrochemistry · 2025Review
- Prospects of Nanoscience with Nanocrystals: 2025 Edition.ACS nano · 2025Review
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed 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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Registered trials
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