The transition to a low-carbon economy demands efficient and sustainable energy-storage solutions, with hydrogen emerging as a promising clean-energy carrier and with metal hydrides recognized for their hydrogen-storage capacity. Here, we leverage machine learning (ML) to predict hydrogen-to-metal (H/M) ratios and solution energy by incorporating thermodynamic parameters and local lattice distortion (LLD) as key features. Our best-performing ML model provides improvements to H/M ratios and solution energies over a broad class of ternary alloys (easily extendable to multi-principal-element alloys), such as Ti-Nb-X (X = Mo, Cr, Hf, Ta, V, Zr) and Co-Ni-X (X = Al, Mg, V). Ti-Nb-Mo alloys reveal compositional effects in H-storage behavior, in particular Ti, Nb, and V enhance H-storage capacity, while Mo reduces H/M and hydrogen weight percent by 40-50%. We attributed to slow hydrogen kinetics in molybdenum rich alloys, which is validated by our pressure-composition isotherm (PCT) experiments on pure Ti and Ti5Mo95 alloys. Density functional theory (DFT) and molecular simulations also confirm that Ti and Nb promote H diffusion, whereas Mo hinders it, highlighting the interplay between electronic structure, lattice distortions, and hydrogen uptake. Notably, our Gradient Boosting Regression model identifies LLD as a critical factor in H/M predictions. To aid material selection, we present two periodic tables illustrating elemental effects on (a) H2 wt% and (b) solution energy, derived from ML, and provide a reference for identifying alloying elements that enhance hydrogen solubility and storage.

Reproduced under a CC BY 3.0 licence. How does an iPhone predict the next word you're going to type in your messages? The technology behind this, and also at the core of many AI applications, is called a transformer; a deep-learning model that handles sequences of data in parallel, and can be fine-tuned for specific tasks. Now, researchers at EPFL and KAIST have created a transformer for Metal-Organic Frameworks (MOFs), a class of porous crystalline materials whose potential applications include energy storage and gas separation. MOFs are composed of thousands of tunable molecular building blocks (metal nodes and organic linkers), and, considering all possible configurations, a vast number of MOFs could potentially be synthesised.

How does an iPhone predict the next word you're going to type in your messages? The technology behind this, and also at the core of many AI applications, is called a transformer; a deep-learning algorithm that detects patterns in datasets. Now, researchers at EPFL and KAIST have created a transformer for Metal-Organic Frameworks (MOFs), a class of porous crystalline materials. By combining organic linkers with metal nodes, chemists can synthesize millions of different materials with potential applications in energy storage and gas separation. The "MOFtransformer" is designed to be the ChatGPT for researchers that study MOFs.

Nowadays, the contemporary manufactured and small unmanned aerial vehicles (UAVs) known as drones are mostly electric-based, using electric engines for their flight power. The application of such propulsion systems need proper elaboration of efficient and light electric energy sources. The paper tends to shift our approach to drones towards one that will see efficient energy storage through the use of hydrogen – which is outlined in the following sections of this article. Speaking of, there are primarily two methods of on-board energy storing in today's drone system: The second method is one on which we are focusing in this article – mostly because of the complexity of the fuel cells and their constant need for the supply of hydrogen. Currently, hydrogen can be stored in compressed state in pressure bottles or in its liquid state (in cryogenic tanks).