State Key Laboratory of Medicinal Chemical Biology, Key Laboratory of Bioactive Materials for the Ministry of Education, College of Life Sciences, Frontiers Science Center for Cell Responses, Nankai University, Tianjin, 300071, China.
School of Medicine, Nankai University, Tianjin, 300071, China.
Adv Mater. 2022 Jul;34(27):e2201736. doi: 10.1002/adma.202201736. Epub 2022 Jun 3.
An abundant number of nanomaterials have been discovered to possess enzyme-like catalytic activity, termed nanozymes. It is identified that a variety of internal and external factors influence the catalytic activity of nanozymes. However, there is a lack of essential methodologies to uncover the hidden mechanisms between nanozyme features and enzyme-like activity. Here, a data-driven approach is demonstrated that utilizes machine-learning algorithms to understand particle-property relationships, allowing for classification and quantitative predictions of enzyme-like activity exhibited by nanozymes. High consistency between predicted outputs and the observations is confirmed by accuracy (90.6%) and R (up to 0.80). Furthermore, sensitive analysis of the models reveals the central roles of transition metals in determining nanozyme activity. As an example, the models are successfully applied to predict or design desirable nanozymes by uncovering the hidden relationship between different periods of transition metals and their enzyme-like performance. This study offers a promising strategy to develop nanozymes with desirable catalytic activity and demonstrates the potential of machine learning within the field of material science.
已经发现大量的纳米材料具有类似酶的催化活性，被称为纳米酶。研究表明，各种内部和外部因素都会影响纳米酶的催化活性。然而，目前还缺乏必要的方法来揭示纳米酶特性与酶样活性之间隐藏的机制。在这里，我们展示了一种数据驱动的方法，利用机器学习算法来理解颗粒特性之间的关系，从而对纳米酶的酶样活性进行分类和定量预测。通过准确性（90.6%）和 R 值（高达 0.80）验证了预测输出与观察结果之间的高度一致性。此外，通过模型的敏感性分析揭示了过渡金属在确定纳米酶活性方面的核心作用。例如，通过揭示不同周期的过渡金属与其酶样性能之间的隐藏关系，该模型成功地应用于预测或设计理想的纳米酶。本研究为开发具有理想催化活性的纳米酶提供了一种有前途的策略，并展示了机器学习在材料科学领域的潜力。
