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Studies of superhydrides by ab-initio and machine learning accelerated random structure searching

Jean-Baptiste Charraud 1 
1 LMCE - Laboratoire Matière sous Conditions Extrêmes
DAM/DIF - DAM Île-de-France, Université Paris-Saclay
Abstract : Alloys characterized by the presence of a large amount of hydrogen in their crystal structure, superhydrides are promising materials for dense energy storage and high temperature superconductivity. The main obstacle to the use of superhydrides for applications remains their very high synthesis pressure, in the range of one million atmosphere. To reduce it, knowledge about these new materials must be deepened. This thesis thus aims at making a contribution to the search of transition metal type superhydrides. For this purpose, the ”Ab-Initio Random Structure Searching” (AIRSS) algorithm was firstly implemented. Studies of hydrides and superhydrides of copper, manganese, yttrium as well as a ternary yttrium-iron system were carried out. The case of yttrium superhydrides is the place of a scientific challenge, because they probably have large primitive cells, which are not accessible by the AIRSS method. To reach a better description of these compounds machine learning tools were developed and used.
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Submitted on : Wednesday, October 27, 2021 - 11:00:13 AM
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Jean-Baptiste Charraud. Studies of superhydrides by ab-initio and machine learning accelerated random structure searching. Materials Science [cond-mat.mtrl-sci]. Université Paris-Saclay, 2021. English. ⟨NNT : 2021UPASF035⟩. ⟨tel-03405328⟩

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