强化人工智能辅助绿色溶剂开发的系统综述,Industrial & Engineering Chemistry Research

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强化人工智能辅助绿色溶剂开发的系统综述
Industrial & Engineering Chemistry Research ( IF 3.8 ) Pub Date : 2023-11-27 , DOI: 10.1021/acs.iecr.3c02305
Huaqiang Wen ₁ , Shihao Nan ₁ , Di Wu ₁ , Quanhu Sun ₁ , Yu Tong ₁ , Jun Zhang ₁ , Saimeng Jin ₁ , Weifeng Shen ₁

Affiliation

溶剂是化工过程中不可或缺的组成部分，环保、安全、高效溶剂的应用对于构建绿色化工过程至关重要。如今，新技术已被应用于发现和探索绿色溶剂，其中人工智能（AI）在预测其物理和化学性质方面发挥着越来越重要的作用。人工智能不仅能够探索绿色溶剂的化学空间，还可以用于筛选预期溶剂并逆向设计新溶剂。本文介绍了人工智能辅助绿色溶剂设计的应用，重点介绍了绿色溶剂设计和性能预测过程中的强化技术。首先，总结了溶剂性质预测过程中采用的定量结构-性质关系（QSPR）的各种强化技术，包括特征提取、集成学习、不确定性分析和可解释性建模的优化和强化。随后，综述了绿色溶剂逆向分子设计应用的基本原理和最新理论进展，包括高通量筛选、计算机辅助分子设计（CAMD）和深度生成模型。最后，对每种强化技术的改进提出了新的想法，以更好地满足绿色溶剂设计中特定应用的高要求。

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A Systematic Review on Intensifications of Artificial Intelligence Assisted Green Solvent Development

Solvents are indispensable components of chemical processes, and the application of ecofriendly, safe, and efficient solvents is vital for building green chemical processes. Nowadays, new techniques have been applied in discovering and exploring green solvents, among which artificial intelligence (AI) plays an increasingly important role in predicting their physical and chemical properties. Being able to explore the chemical space of green solvents, AI can also be utilized to screen out expected solvents and inversely design new solvents. This review introduces the application of AI assisted green solvent design, focusing on intensification techniques in the processes of green solvent design and property prediction. First, the various intensification techniques of quantitative structure–property relationships (QSPR) employed in the process of solvent property prediction are summarized, including the optimization and intensification of feature extraction, ensemble learning, uncertainty analysis, and interpretability modeling. After that, the basic principles and latest theoretical advances in the application of inverse molecular design for green solvents are reviewed, including high-throughput screening, computer-aided molecular design (CAMD), and deep generative models. Finally, new ideas are proposed for the improvement of each intensification technique in order to better match the high demands of the particular application in green solvent design.

更新日期：2023-11-27

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