A mini review on revolutionizing hydrogenation catalysis: unleashing transformative power of artificial intelligence.

Abstract

Listen

Translate article

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.