Computationally Predicted High-Throughput Free-Energy Phase Diagrams for the Discovery of Solid-State Hydrogen Storage Reactions

Computational analysis of the enhancement of photoelectrolysis using transition metal dichalcogenide heterostructures

Finding a material with all the desired properties for a photocatalytic water splitter is a challenge yet to be overcome, requiring both a surface with ideal energetics for all steps in the hydrogen and oxygen evolution reactions (HER and OER) and a bulk band gap large enough to mediate said steps. We have instead examined separating these challenges by investigating the energetic properties of two-dimensional transition metal dichalcogenides (TMDCs) that could be used as a surface coating to a material with a large enough bulk band gap. First we investigated the energetics of monolayer MoS₂and PdSe₂using density functional theory and then investigated how these energetics changed when they were combined into a heterostructure. Our results show that the surface properties were practically (<0.2 eV) unchanged when combined and the MoS₂layer aligns well with the OER and HER. This work highlights the potential of TMDC monolayers as surface coatings for bulk materials that have sufficient band gaps for photocatalytic applications.

Dynamics of Heterogeneous Catalytic Processes at Operando Conditions

The rational design of high-performance catalysts is hindered by the lack of knowledge of the structures of active sites and the reaction pathways under reaction conditions, which can be ideally addressed by an in situ/operando characterization. Besides the experimental insights, a theoretical investigation that simulates reaction conditions-so-called operando modeling-is necessary for a plausible understanding of a working catalyst system at the atomic scale. However, there is still a huge gap between the current widely used computational model and the concept of operando modeling, which should be achieved through multiscale computational modeling. This Perspective describes various modeling approaches and machine learning techniques that step toward operando modeling, followed by selected experimental examples that present an operando understanding in the thermo- and electrocatalytic processes. At last, the remaining challenges in this area are outlined.

Artificial Intelligence Application in Solid State Mg-Based Hydrogen Energy Storage

The use of Mg-based compounds in solid-state hydrogen energy storage has a very high prospect due to its high potential, low-cost, and ease of availability. Today, solid-state hydrogen storage science is concerned with understanding the material behavior of different compositions and structure when interacting with hydrogen. Finding a suitable material has remained an elusive idea, and therefore, this review summarizes works by various groups, the milestones they have achieved, and the roadmap to be taken on the study of hydrogen storage using low-cost magnesium composites. Mg-based compounds are further examined from the perspective of artificial intelligence studies, which helps to improve prediction of their properties and hydrogen storage performance. There exist several techniques to improve the performance of Mg-based compounds: microstructure modification, use of catalytic additives, and composition regulation. Microstructure modification is usually achieved by employing different synthetic techniques like severe plastic deformation, high energy ball milling, and cold rolling, among others. These synthetic approaches are discussed herein. In this review, a discussion of key parameters and operating conditions are highlighted in a view to finding high storage capacity and faster kinetics. Furthermore, recent approaches like machine learning have found application in guiding the experimental design. Hence, this review paper also explores how machine learning techniques have been utilized to fasten the materials research. It is however noted that this study is not exhaustive in itself.