A combined experimental and machine learning exploration of Ti2-xZrxMnCrFeNi high entropy Laves hydrides

A series of high entropy AB2-type Ti2-xZrxMnCrFeNi alloys (x = 0.6, 0.7, 0.8, 0.9, 1.0, 1.1 and 1.2) were synthesized to investigate their potential for hydrogen storage and chemical compression. The influence of the Ti/Zr ratio was explored in terms of structural, microstructural and thermodynamic properties. The storage capacity together with the reaction enthalpy and entropy changes of the synthesized high entropy alloys were compared to predictions from Machine Learning (ML) to investigate changes in these properties across the explored composition space. The results revealed that a decreasing Zr content consistently lowered the hydride formation enthalpy and increased the plateau pressure from 8 to >90 bar H2 at 25 °C, in good agreement with ML predictions. Selected compositions (x = 1.0 and 1.2) demonstrated reversible hydrogen storage capability over 150 cycles, with capacities of 1.34–1.40 wt % H2 and remarkable reaction kinetics (<4 min) at ambient temperature. These experimental and computational findings highlight the potential of this Laves-HEA system as tuneable, stable, and cost-effective materials suitable for long-term operations in stationary hydrogen storage and compression applications.

Authors

Institutions

Publication Details

Journal
Materialia
Published
2025-04-18
DOI
https://doi.org/10.1016/j.mtla.2025.102414
Citations
4
Primary Topic
High Entropy Alloys Studies
Type
article
Field-Weighted Citation Impact
2.85
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

A combined experimental and machine learning exploration of Ti2-xZrxMnCrFeNi high entropy Laves hydrides

Jin‐Yoo Suh, Julien O. Fadonougbo, Vitalie Stavila, Martin Sahlberg et al.
4 citations
Materialia
High Entropy Alloys Studies
2.85
article

A combined experimental and machine learning exploration of Ti2-xZrxMnCrFeNi high entropy Laves hydrides

Jin‐Yoo Suh, Julien O. Fadonougbo, Vitalie Stavila, Martin Sahlberg, Paulo H. B. Brant Carvalho, Veronica Enblom, Sang M. Han, Taejun Ha, Rebecca Clulow, Matthew Witman, Lauren Way
article en
4 citations

Abstract

A series of high entropy AB2-type Ti2-xZrxMnCrFeNi alloys (x = 0.6, 0.7, 0.8, 0.9, 1.0, 1.1 and 1.2) were synthesized to investigate their potential for hydrogen storage and chemical compression. The influence of the Ti/Zr ratio was explored in terms of structural, microstructural and thermodynamic properties. The storage capacity together with the reaction enthalpy and entropy changes of the synthesized high entropy alloys were compared to predictions from Machine Learning (ML) to investigate changes in these properties across the explored composition space. The results revealed that a decreasing Zr content consistently lowered the hydride formation enthalpy and increased the plateau pressure from 8 to >90 bar H2 at 25 °C, in good agreement with ML predictions. Selected compositions (x = 1.0 and 1.2) demonstrated reversible hydrogen storage capability over 150 cycles, with capacities of 1.34–1.40 wt % H2 and remarkable reaction kinetics (<4 min) at ambient temperature. These experimental and computational findings highlight the potential of this Laves-HEA system as tuneable, stable, and cost-effective materials suitable for long-term operations in stationary hydrogen storage and compression applications.

MaterialiaVol. 40
Uppsala University (SE), Seoul National University (KR), Sandia National Laboratories California (US), Korea Institute of Science and Technology (KR), Korea Institute of Industrial Technology (KR)
Openalex Percentile: Top 10%
High Entropy Alloys Studies
2.85
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.