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Supervised deep learning prediction of the formation enthalpy of the full set of configurations in complex phases: the phase as an example
Jean-Claude Crivello, Nataliya Sokolovska, Jean-Marc Joubert
Machine learning (ML) methods are becoming integral to scientific inquiry in numerous disciplines, such as material sciences. In this manuscript, we demonstrate how ML can be used…
On the structure and electronic properties of FeVWAl thin films
E. Alleno, A. Berche, J. -C. Crivello +2
A very large thermoelectric figure of merit ZT = 6 at 380 K has recently been reported in FeVWAl under thin-film form (Hinterleitner et al., Nature 576 (2019) 8…
Materials for hydrogen-based energy storage: Past, recent progress and future outlook
Volodymyr A. Yartys, Marcello Baricco, Jose Bellosta von Colbe +45
Magnesium hydride owns the largest share of publications on solid materials for hydrogen storage. The Magnesium group of international experts contributing to IEA Task 32 Hydrogen…
Correlations between stacked structures and weak itinerant magnetic properties of La Y Ni compounds
Valérie Paul-Boncour, Jean-Claude Crivello, Nicolas Madern +5
Hexagonal LaNi and rhombohedral YNi are weak itinerant antiferromagnet (wAFM) and ferromagnet (wFM), respectively. The crystal structure and magnetic properties of…
Relation between the weak itinerant magnetism in Ni compounds ( = Y, La) and their stacked crystal structures
Jean-Claude Crivello, Valérie Paul-Boncour
The weak itinerant magnetic properties of Ni compounds with = {Y, La} have been investigated using electronic band structure calculations in the relation with their po…