Machine learning driven search of hydrogen storage materials

2025-03-06

AI Summary

Efficient screening of solid-state hydrogen storage materials for low-carbon energy transition remains challenging. Method: This study proposes a novel machine learning paradigm integrating thermodynamic parameters with local lattice distortion (LLD) features—introducing LLD as a key physical descriptor for predicting the hydrogen-to-metal (H/M) ratio in metal hydrides. An element-level periodic table tool is developed to quantitatively elucidate compositional effects on gravimetric capacity and hydrogen dissolution energy. Gradient boosting regression models are trained using DFT calculations, molecular dynamics simulations, and pressure–composition–temperature (PCT) experimental data across Ti–Nb–X and Co–Ni–X alloy systems. Results: The models achieve high predictive accuracy for H/M ratio and dissolution energy (R² > 0.92). Ti, Nb, and V are identified as strong hydrogen absorbers; Mo reduces H/M by 40–50%. Excellent agreement is observed between theoretical predictions and experimental hydrogenation kinetics.

Technology Category

• Machine Learning: Hardware-aware ML
• Humans and AI: Human-in-the-loop Machine Learning
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

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.

Problem

• Machine learning predicts hydrogen storage in metal hydrides.
• Identifies key elements enhancing hydrogen storage capacity.
• Explores effects of lattice distortion on hydrogen uptake.

Innovation

• Machine learning predicts H/M ratios and solution energies.
• Incorporates thermodynamic parameters and lattice distortion.
• Identifies alloying elements enhancing hydrogen storage capacity.