Machine Learning and Density Functional Theory for Catalyst and Process Design in Hydrogen Production

Hydrogen plays a vital role in achieving NetZero emissions as a carbon-free energy carrier. However, its production, especially green hydrogen generated from renewable sources, is hindered by low efficiency and limited yield, primarily due to the performance of the catalysts used. Developing efficient catalysts typically involves extensive experimental work and trial-and-error processes. For instance, screening for effective catalysts still heavily relies on human-lab-work, a process that is time-consuming. Facing this critical challenge, machine learning (ML) emerges as a promising solution. ML, a core component of data mining and analysis that uses statistical algorithms without explicit instructions, can rationalize the design of catalysts through the use of big data, including DFT results. This approach makes a significant shift from traditional trial-and-error approaches to more computationally driven strategies, offering a more effective path to uncovering vital methodologies for catalyst development. This review aims to capture and evaluate the impact of ML algorithms that have driven progress in catalyst research over the past three years. It presents an overview of the existing ML algorithms, exploring their specific functionalities, benefits, and limitations. Besides, this review also considers prospective solutions and future directions for applying ML to enhance the efficiency of green hydrogen production, particularly through electrochemical and biological processes.