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Related Experiment Video

Updated: Jun 4, 2025

Quantification of Hydrogen Concentrations in Surface and Interface Layers and Bulk Materials through Depth Profiling with Nuclear Reaction Analysis

Quantification of Hydrogen Concentrations in Surface and Interface Layers and Bulk Materials through Depth Profiling with Nuclear Reaction Analysis

Published on: March 29, 2016

Machine Learning in Solid-State Hydrogen Storage Materials: Challenges and Perspectives.

Panpan Zhou^1,2, Qianwen Zhou², Xuezhang Xiao^2,3

¹College of Materials Science and Engineering, Hohai University, Changzhou, Jiangsu, 213200, China.

Advanced Materials (Deerfield Beach, Fla.)

|December 20, 2024

Summary

This summary is machine-generated.

Machine learning (ML) accelerates the discovery of high-performance solid-state hydrogen storage materials (HSMs). This review details ML applications, challenges, and a roadmap for sustainable energy storage solutions.

Keywords:

high‐throughput material design hydrogen storage materials machine learning mechanism mining

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Area of Science:

Materials Science
Computational Chemistry
Energy Storage

Background:

High-performance solid-state hydrogen storage materials (HSMs) are critical for sustainable energy.
Machine learning (ML) offers powerful tools to overcome limitations in HSM research.
Key challenges include low storage capacity and difficult cycling conditions.

Purpose of the Study:

To review the current state of ML applications in solid-state hydrogen storage.
To identify challenges and propose solutions for ML in HSM research.
To outline a future roadmap for ML-driven HSM development.

Main Methods:

Literature review of ML models and datasets for HSMs.
Analysis of ML applications across various HSM classes (e.g., Ti-based, Mg-based).
Critical evaluation of data quality, availability, and model interpretability.

Main Results:

ML successfully addresses issues like low capacity and cycling conditions in HSMs.
Demonstrated ML's role in exploiting composition-structure-property relationships.
Highlighted a Ti-based HSM optimized for fuel cell applications via high-throughput screening.

Conclusions:

ML is a pivotal tool for designing novel HSMs.
Addressing data limitations and model interpretability is crucial for advancing ML in this field.
A strategic roadmap can enhance ML's impact on efficient and sustainable hydrogen storage.