Maleki Reza, Shams Seyed Mohammadreza, Chellehbari Yasin Mehdizadeh, Rezvantalab Sima, Jahromi Ahmad Miri, Asadnia Mohsen, Abbassi Rouzbeh, Aminabhavi Tejraj, Razmjou Amir
Department of Chemical Engineering, Shiraz University, Shiraz, Iran.
Department of Petroleum Engineering, Amirkabir University of Technology, Tehran, Iran.
Commun Chem. 2022 Oct 20;5(1):132. doi: 10.1038/s42004-022-00744-x.
Significant attempts have been made to improve the production of ion-selective membranes (ISMs) with higher efficiency and lower prices, while the traditional methods have drawbacks of limitations, high cost of experiments, and time-consuming computations. One of the best approaches to remove the experimental limitations is artificial intelligence (AI). This review discusses the role of AI in materials discovery and ISMs engineering. The AI can minimize the need for experimental tests by data analysis to accelerate computational methods based on models using the results of ISMs simulations. The coupling with computational chemistry makes it possible for the AI to consider atomic features in the output models since AI acts as a bridge between the experimental data and computational chemistry to develop models that can use experimental data and atomic properties. This hybrid method can be used in materials discovery of the membranes for ion extraction to investigate capabilities, challenges, and future perspectives of the AI-based materials discovery, which can pave the path for ISMs engineering.
人们已做出大量努力来提高离子选择性膜（ISM）的生产效率并降低成本，而传统方法存在局限性、实验成本高和计算耗时等缺点。消除实验局限性的最佳方法之一是人工智能（AI）。本综述讨论了AI在材料发现和ISM工程中的作用。AI可以通过数据分析将实验测试的需求降至最低，从而基于使用ISM模拟结果的模型加速计算方法。与计算化学相结合，使AI能够在输出模型中考虑原子特征，因为AI充当实验数据和计算化学之间的桥梁，以开发能够使用实验数据和原子特性的模型。这种混合方法可用于离子提取膜的材料发现，以研究基于AI的材料发现的能力、挑战和未来前景，这可为ISM工程铺平道路。
