材料科学
氢气储存
高熵合金
氢
空格(标点符号)
冶金
热力学
结晶学
工程物理
合金
物理
计算机科学
量子力学
操作系统
化学
作者
Andrei Agafonov,Nayely Pineda-Romero,Matthew Witman,Veronica Enblom,Martin Sahlberg,Vivian Nassif,Lei Lei,David M. Grant,Martin Dornheim,Sanliang Ling,Vitalie Stavila,Claudia Zlotea
标识
DOI:10.1021/acsami.5c08574
摘要
This study reports on the search for the most promising alloys in the compositional space of (TiVNb)80Cr20-xMox (x = 5, 10, and 15) and (TiVNb)75Cr25-xMox (x = 5, 10, 15, and 20) high-entropy alloys. First, data-driven machine learning applied to these systems predicts that increasing the Mo content destabilizes the enthalpy of the hydride phases. Second, experimental and density functional theory (DFT) validations were performed. The as-prepared alloys have single-phase bcc lattices and rapidly absorb hydrogen to form fcc-type hydrides with a high capacity between 1.6 and 2.0 H/M. Despite a positive effect on the thermodynamics of the hydride phases, increasing the Mo content in these alloys has a negative effect on the maximum capacity. The cycling experiments highlight the need to balance the reversible capacity, cycle life, and crystalline stabilities of these phases. Therefore, considering all these results, the most promising alloy with trade-off properties within the targeted compositional space has been identified to be (TiVNb)75Cr5Mo20 that shows a maximum capacity of 2.6 wt % (1.8 H/M), a reasonable enthalpy of hydride formation (-38.6 kJ/mol H2), and a notable gravimetric reversible capacity of 1.42 wt % at room temperature. To identify the most promising high-entropy alloys for this application, integrated machine learning predictions followed by experimental and DFT validations proved to be an effective strategy.
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