Efficiently predicting pressure-composition-temperature diagrams to discover low-stability metal hydrides

Lauren Way; Véronique Charbonnier; Julien O. Fadonougbo; Rebecca Clulow; Lei Lei; Sanliang Ling; David Grant; Martin Dornheim; Tanumoy Banerjee; Hanna Breunig; Claudia Zlotea; Andrew J. E. Rowberg; Pin-Wen Guan; Vitalie Stavila; Mark D. Allendorf; Martin Sahlberg; Kouji Sakaki; NORMAN C BARTELT; Matthew D. Witman; Lauren Way; Véronique Charbonnier; Julien O. Fadonougbo; Rebecca Clulow; Lei Lei; Sanliang Ling; David Grant; Martin Dornheim; Tanumoy Banerjee; Hanna Breunig; Claudia Zlotea; Andrew J. E. Rowberg; Pin-Wen Guan; Vitalie Stavila; Mark D. Allendorf; Martin Sahlberg; Kouji Sakaki; NORMAN C BARTELT; Matthew D. Witman

doi:10.1039/d5ta06645j

Key Points

Data-driven models are addressed regarding their limitations in predicting pressure-composition-temperature diagrams.
The approach highlights optimization to enhance out-of-distribution prediction accuracy among compositionally complex alloys.
The findings suggest a need for more efficient forecasting mechanisms to discover low-stability metal hydrides effectively.
By refining prediction algorithms, it may enable breakthroughs in the synthesis and application of advanced materials.

Implication

This analysis demonstrates improved prediction accuracy of low-stability metal hydrides in alloys, indicating data-driven models may require refinement.

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Cite This Study

Way et al. (2025) studied this question.

synapsesocial.com/papers/694020e82d562116f28facee https://doi.org/10.1039/d5ta06645j

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Efficiently predicting pressure-composition-temperature diagrams to discover low-stability metal hydrides

Key Points

Implication

Discussion

Cite This Study

Efficiently predicting pressure-composition-temperature diagrams to discover low-stability metal hydrides

Key Points

Implication

Discussion

Cite This Study

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