Mishra Adarsh Sushil, Lade Vikesh Gurudas, Barmar Jyoti Ramesh, Bindwal Ankush Babarao, Birmod Ramesh Pandharinath
Department of Chemical Engineering, Laxminarayan Innovation Technological University, Bharat Nagar, Nagpur, 440 033, Maharashtra, India.
Distillate and Heavy Oil Processing Division, CSIR Indian Institute of Petroleum Mohkampur, Haridwar Road, Dehradun, 248 005, Uttarakhand, India.
J Mol Model. 2025 Apr 30;31(5):152. doi: 10.1007/s00894-025-06376-x.
The field of hydrogenation catalysis has undergone a revolution due to the application of artificial intelligence (AI) and machine learning (ML), which have opened up new avenues for improving catalyst design, reaction efficiency, and pathway optimization. Trial-and-error techniques are a major component of traditional catalyst discovery methods, and they can be resource and time-intensive when it comes to real-world applications. On the other hand, real-time reaction condition optimization, predictive modelling, and quicker catalyst screening are made possible by AI-based techniques. Using methods like neural networks, Bayesian optimization, and generative models, this paper emphasizes how artificial intelligence has been revolutionizing catalyst creation along with mechanistic knowledge and process intensification. AI has the ability to completely transform catalytic research, as demonstrated by a number of case studies that demonstrate its use in CO₂ hydrogenation, biomass upgrading, and metal catalyzed reactions.
This review synthesizes recent developments in AI-enhanced catalytic modelling, kinetic parameter estimation, and multi-scale reaction simulations and explores machine learning models such as Random Forest, Gradient Boosting, Artificial Neural Networks, and Gaussian Processes to predict key catalytic performance indicators. Additionally, high-throughput simulated screening and computational methods such as Density Functional Theory simulations and molecular descriptor-based modelling have been used to improve catalyst design tactics. Summary of the ML models which were trained and validated using open source frameworks such as scikit-learn, TensorFlow, and PyTorch is also presented in this paper. Most of the research studies datasets were using the resource data from Catalysis Hub and the materials project. Techniques for data processing and pre-processing include methods for choosing the component features, such as d-band center analysis, adsorption energy calculations, and algorithm normalization. This review study consists of an in-depth analysis of how data-driven modelling improves catalyst performance, and its prediction and optimization in hydrogenation catalysis reactions by artificial intelligence and machine learning driven approaches.
由于人工智能（AI）和机器学习（ML）的应用，氢化催化领域经历了一场变革，为改进催化剂设计、反应效率和路径优化开辟了新途径。反复试验技术是传统催化剂发现方法的一个主要组成部分，在实际应用中可能会耗费资源和时间。另一方面，基于人工智能的技术使实时反应条件优化、预测建模和更快的催化剂筛选成为可能。本文通过神经网络、贝叶斯优化和生成模型等方法，强调了人工智能如何与机理知识和过程强化一起彻底改变催化剂的创制。人工智能有能力彻底改变催化研究，一些案例研究证明了其在二氧化碳氢化、生物质升级和金属催化反应中的应用。
本综述综合了人工智能增强催化建模、动力学参数估计和多尺度反应模拟的最新进展，并探索了随机森林、梯度提升、人工神经网络和高斯过程等机器学习模型来预测关键催化性能指标。此外，高通量模拟筛选和密度泛函理论模拟及基于分子描述符的建模等计算方法已被用于改进催化剂设计策略。本文还总结了使用scikit-learn、TensorFlow和PyTorch等开源框架进行训练和验证的机器学习模型。大多数研究数据集使用了来自催化中心和材料项目的资源数据。数据处理和预处理技术包括选择成分特征的方法，如d带中心分析、吸附能计算和算法归一化。本综述研究深入分析了数据驱动建模如何通过人工智能和机器学习驱动的方法提高催化剂性能及其在氢化催化反应中的预测和优化。
