Machine learning assisted phase and size-controlled synthesis of iron oxide particles
Machine learning assisted phase and size-controlled synthesis of iron oxide particles
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DOI：
10.1016/j.cej.2023.145216
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发表时间：
2023-03
影响因子：
15.1
通讯作者：
Juejing Liu;Zimeng Zhang;Xiaoxu Li;M. Zong;Yining Wang;Su Wang;Ping Chen;Zaoyan Wan;Yatong Zhao;Lili Liu;Yangang Liang;Wen Wang;Zhemin Wang;Shiren Wang;Xiaofeng Guo;Emily Saldanha;K. Rosso;Xin Zhang
中科院分区：
文献类型：
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作者：
Juejing Liu;Zimeng Zhang;Xiaoxu Li;M. Zong;Yining Wang;Su Wang;Ping Chen;Zaoyan Wan;Yatong Zhao;Lili Liu;Yangang Liang;Wen Wang;Zhemin Wang;Shiren Wang;Xiaofeng Guo;Emily Saldanha;K. Rosso;Xin Zhang
合成具有特定相和粒度的氧化铁是材料科学、储能、生物医学应用、环境科学和地球科学等各个领域的关键挑战。然而，尽管在这一领域取得了重大进展，但目前的许多粒子结果都是基于对合成条件的耗时的试错探索。本研究旨在探索一种非常不同的方法：1)基于机器学习（ML）技术从指定的反应参数预测合成结果；2)通过新设计的推荐算法将参数集关联以获得具有期望结果的产品。为了实现这一点，我们测试了四种机器学习算法，即随机森林、逻辑回归、支持向量机和k近邻。在这些模型中，随机森林在预测测试数据集中氧化铁颗粒的相位和大小时的准确率分别达到96%和81%，优于其他模型。令人惊讶的是，排列特征重要性分析显示，体积（可能与压力密切相关）是重要特征之一，以及前驱体浓度、pH、温度和时间，影响合成过程中氧化铁颗粒的相和尺寸。为了验证随机森林模型的鲁棒性，在数据集中未包含的加性和非加性系统中，基于24种随机生成方法对预测结果和实验结果进行了比较。模型预测的产物相和粒度与实验结果吻合较好。此外，开发了一种搜索和排序算法，以推荐潜在的合成参数，以从数据集中的先前研究中获得具有所需相和粒度的氧化铁产品。本研究为材料合成和制备的闭环方法奠定了基础，从数据集中提出潜在的反应参数并预测可能的结果，然后进行实验和分析，最终丰富数据集。
Synthesis of iron oxides with specific phases and particle sizes is a crucial challenge in various fields, including materials science, energy storage, biomedical applications, environmental science, and earth science. However, despite significant advances in this area, much of the current palette of particle outcomes has been based on time-consuming trial-and-error exploration of synthesis conditions. The present study was designed to explore a very different approach to 1) predict the outcome of synthesis from specified reaction parameters based on using machine learning (ML) techniques, and 2) correlate sets of parameters to obtain products with desired outcomes by a newly designed recommendation algorithm. To achieve this, four ML algorithms were tested, namely random forest, logistic regression, support vector machine, and k-nearest neighbor. Among the models, random forest outperformed the others, attaining 96% and 81% accuracy when predicting the phase and size of iron oxide particles in the test dataset. Surprisingly, the permutation feature importance analysis revealed that volume, which may strongly relate to pressure, was one of the important features, along with precursor concentration, pH, temperature, and time, influencing the phase and size of iron oxide particles during synthesis. To verify the robustness of the random forest models, prediction and experimental results were compared based on 24 randomly generated methods in additive and non-additive systems not included in the datasets. The predictions of product phase and particle size from the models agreed well with the experimental results. Furthermore, a searching and ranking algorithm was developed to recommend potential synthesis parameters for obtaining iron oxide products with the desired phase and particle size from previous studies in the dataset. This study lays the foundation for a closed-loop approach in materials synthesis and preparation, beginning with suggesting potential reaction parameters from the dataset and predicting potential outcomes, followed by conducting experiments and analyses, and ultimately enriching the dataset.