Computational screening of layered metal chalcogenide materials for HER electrocatalysts, and its synergy with experiments.

Layered materials have emerged as attractive candidates in our search for abundant, inexpensive and efficient hydrogen evolution reaction (HER) catalysts, due to larger specific area these offer. Among these, transition metal dichalcogenides have been studied extensively, while ternary transition metal tri-chalcogenides have emerged as promising candidates recently. Computational screening has emerged as a powerful tool to identify the promising materials out of an initial set for specific applications, and has been employed for identifying HER catalysts also. This article presents a comprehensive review of how computational screening studies based on density functional calculations have successfully identified the promising materials among the layered transition metal di- and tri-chalcogenides. Synergy of these computational studies with experiments is also reviewed. It is argued that experimental verification of the materials, predicted to be efficient catalysts but not yet tested, will enlarge the list of materials that hold promise to replace expensive platinum, and will help ushering in the much awaited hydrogen economy.

MagGen: A Graph-Aided Deep Generative Model for Inverse Design of Permanent Magnets.

A deep generative model based on a variational autoencoder (VAE), conditioned simultaneously by two target properties, is developed to inverse design stable magnetic materials. The structure of the physics-informed, property embedded latent space of the model is analyzed using graph theory. An impressive ∼96% of the generated materials are found to satisfy the target properties as per predictions from the target-learning branches. This is a huge improvement over approaches that do not condition the VAE latent space by target properties or that do not consider the connectivity of the parent materials from which the new materials are generated. This impressive feat is achieved by using a simple real-space-only representation that can be directly read from material cif files. Model predictions are finally validated by density functional theory calculations on a randomly chosen subset of materials. The performance of the present model is comparable or superior to that of models reported earlier. This model (MagGen) is applied to the problem of designing rare earth-free permanent magnets with promising results.