Selecting Machine Learning Models to Support the Design of Al/CuO Nanothermites - Équipe Nano-ingénierie et intégration des oxydes métalliques et de leurs interfaces Accéder directement au contenu
Article Dans Une Revue Journal of Physical Chemistry A Année : 2022

Selecting Machine Learning Models to Support the Design of Al/CuO Nanothermites

Résumé

Novel properties associated with nanothermites have attracted great interest for several applications, including lead-free primers and igniters. However, the prediction of quantitative structure-energetic performance relationships are still challenging. This study investigates machine learning methods as tools to surrogate complex physical models to design novel nanothermites with optimized burning rates chosen for energetic performance. The study
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Dates et versions

hal-03574270 , version 1 (15-02-2022)

Identifiants

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Yasser Sami, Nicolas Richard, David Gauchard, Alain Estève, Carole Rossi. Selecting Machine Learning Models to Support the Design of Al/CuO Nanothermites. Journal of Physical Chemistry A, 2022, 126 (7), pp.1245-1254. ⟨10.1021/acs.jpca.1c09520⟩. ⟨hal-03574270⟩
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