Knowing the correlation of reaction parameters in the preparation process of carbon dots (CDs) is essential for optimizing the synthesis strategy, exploring exotic properties, and exploiting potential applications. However, the integrated screening experimental data on the synthesis of CDs are huge and noisy. Machine learning (ML) has recently been successfully used for the screening of high-performance materials. Here, we demonstrate how ML-based techniques can offer insight into the successful prediction, optimization, and acceleration of CDs’ synthesis process. A regression ML model on hydrothermal-synthesized CDs is established capable of revealing the relationship between various synthesis parameters and experimental outcomes as well as enhancing the process-related properties such as the fluorescent quantum yield (QY). CDs exhibiting a strong green emission with QY up to 39.3% are obtained through the combined ML guidance and experimental verification. The mass of precursors and the volume of alkaline catalysts are identified as the most important features in the synthesis of high-QY CDs by the trained ML model. The CDs are applied as an ultrasensitive fluorescence probe for monitoring the Fe³⁺ ion because of their superior optical behaviors. The probe exhibits the linear response to the Fe³⁺ ion with a wide concentration range (0–150 μM), and its detection limit is 0.039 μM. Our findings demonstrate the great capability of ML to guide the synthesis of high-quality CDs, accelerating the development of intelligent material.

中文翻译：

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机器学习驱动的碳点合成具有增强的量子产率。

知道碳点（CD）制备过程中反应参数的相关性对于优化合成策略，探索奇特性质和开发潜在应用至关重要。然而，关于CD合成的综合筛选实验数据庞大且嘈杂。机器学习（ML）最近已成功用于筛选高性能材料。在这里，我们演示了基于ML的技术如何能够洞悉CD合成过程的成功预测，优化和加速。建立了水热合成CD的回归ML模型，该模型能够揭示各种合成参数与实验结果之间的关系，并能增强与过程相关的特性，例如荧光量子产率（QY）。通过ML指导和实验验证相结合，可以得到QY高达39.3％的具有强绿色发射的CD。前者的质量和碱性催化剂的体积被确定为通过训练有素的ML模型合成高QY CD的最重要特征。CD用作监测Fe的超灵敏荧光探针³⁺离子具有出色的光学性能。该探针在较宽的浓度范围（0-150μM）内对Fe ³⁺离子表现出线性响应，其检测极限为0.039μM。我们的发现证明了ML指导高质量CD合成的巨大能力，从而加速了智能材料的开发。