Machine learning models are increasingly being applied to predict the properties of nanocrystals synthesized via hydrothermal methods, offering a data-driven approach to optimize synthesis conditions and outcomes. Hydrothermal synthesis involves the crystallization of materials from aqueous solutions at elevated temperatures and pressures, producing nanocrystals with controlled size, morphology, and composition. Key input parameters in machine learning models typically include precursor concentrations, reaction temperature, pressure, pH, reaction time, and surfactant or capping agent ratios. Output targets focus on nanocrystal characteristics such as particle size, yield, crystallinity, shape, and phase purity.

Precursor ratios play a critical role in determining nanocrystal composition and morphology. For example, in the synthesis of metal oxide nanocrystals like ZnO or TiO2, the molar ratio of metal precursors to hydroxide or other reducing agents directly influences nucleation and growth kinetics. Machine learning models trained on datasets incorporating these ratios can predict how variations affect particle size distribution. Reaction temperature is another crucial parameter, as it governs reaction kinetics and thermodynamic stability. Models have shown that higher temperatures often lead to increased crystallinity but may also result in larger particle sizes due to accelerated Ostwald ripening.

Successful case studies demonstrate the efficacy of machine learning in hydrothermal nanocrystal synthesis. One study focused on predicting the size of cerium oxide nanoparticles using a random forest model trained on historical synthesis data. Input features included cerium nitrate concentration, reaction temperature, and hydrothermal duration, while the output was the average particle diameter. The model achieved a prediction accuracy within ±2 nm for particles ranging from 5 to 30 nm. Another study applied a neural network to optimize the yield of gold nanocrystals, using gold chloride concentration, reducing agent ratio, and temperature as inputs. The model identified an optimal temperature window of 150–180°C, maximizing yield while minimizing aggregation.

Data scarcity remains a significant challenge in developing robust machine learning models for hydrothermal synthesis. Unlike more standardized synthesis methods, hydrothermal experiments often vary in reactor design, heating profiles, and mixing conditions, leading to inconsistencies in reported data. Many published studies lack comprehensive metadata, such as precise heating rates or stirring speeds, which are critical for model training. Transfer learning has been explored as a solution, where models pre-trained on larger datasets from related synthesis methods are fine-tuned with limited hydrothermal data. However, this approach requires careful feature alignment to avoid introducing bias.

Another challenge is the multi-objective nature of nanocrystal optimization. Researchers often seek to balance competing outcomes, such as maximizing yield while minimizing particle size. Machine learning models employing Pareto optimization techniques have been used to identify trade-offs between these objectives. For instance, a support vector machine model applied to iron oxide nanocrystal synthesis successfully predicted conditions that achieved high yield (>85%) with particle sizes below 20 nm by adjusting pH and reaction time simultaneously.

Despite these advances, interpretability of machine learning models remains an area of active research. While neural networks offer high predictive accuracy, their black-box nature complicates the extraction of mechanistic insights. In contrast, decision tree-based models provide more transparent rules, such as identifying threshold temperatures beyond which particle agglomeration becomes dominant. Hybrid approaches combining mechanistic modeling with machine learning are emerging as a promising direction, integrating domain knowledge with data-driven predictions.

Future progress in this field will depend on improved data sharing practices and standardized reporting of hydrothermal synthesis parameters. Collaborative databases aggregating synthesis conditions and corresponding nanocrystal properties could significantly enhance model generalizability. Additionally, active learning strategies, where models suggest the most informative next experiments, may accelerate the optimization process while reducing experimental costs.

In summary, machine learning offers powerful tools for predicting and optimizing hydrothermal nanocrystal properties, leveraging synthesis parameters to forecast outcomes like size and yield. While challenges such as data scarcity and model interpretability persist, continued advancements in algorithmic approaches and data infrastructure hold promise for more efficient and reliable nanocrystal design.