2026-07-01T18:02:36Z https://archive.materialscloud.org/oai2d

oai:materialscloud.org:ejv7m-q7928 2026-01-05T16:10:09Z openaire_data community-mcarchive

Brorsson, Joakim Hellman, Anders Brorsson, Joakim Hellman, Anders Leion, Henrik 2026-01-05 <p>A key technical challenge inhibiting fossil-free energy generation, which is especially pronounced  in solar plants, is the inherent intermittency. Since thermochemical energy storage represents a  solution that has yet to see large-scale utilization, the goal of the study at hand has been to find  suitable materials using a hybrid high-throughput approach, encompassing both data mining and  first principles calculations. Through these efforts, thermal properties have been estimated for more  than 50 000 mono-, bi-, and trimetallic oxides, as well as alloys, retrieved from the Materials Project  database.<br>Thereafter, close to 18 000 convex hulls were created, each representing a unique metal ratio, in order  to determine the most stable phases at different oxygen chemical potentials. In turn, this allowed  almost 300 000 potential transitions to be identified. After filtering out potentially toxic and rare  compositions, a little over 51 000 remained, which were further reduced, to about 3000, by requiring  that the phase transformation must occur at a temperature between 400 °C and 1300 °C.<br>A thorough analysis of the results led to the discovery of several promising energy storage materials.  In addition, Chromium; manganese; calcium; and magnesium were found to be associated with high  reaction enthalpies. When the sensible heat was taken into account, however, light elements such as  lithium; boron; iron; and sodium dominated among the top-ranking candidates. This study therefore  demonstrates how high-throughput data mining can be efficiently enhanced through first-principles  calculations, enabled by machine-learning interatomic potentials, to facilitate material discovery.</p> application/zip text/markdown https://doi.org/10.24435/materialscloud:4w-qs oai:materialscloud.org:ejv7m-q7928 mcid:2026.4 eng Materials Cloud https://archive.materialscloud.org/communities/mcarchive https://doi.org/10.24435/materialscloud:tt-3e info:eu-repo/semantics/openAccess Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode data-mining thermochemical energy storage first-principles thermodynamics metal oxides high-throughput machine learning potentials Discovery of thermochemical energy storage materials via a hybrid data-mining approach info:eu-repo/semantics/other