Extracting an empirical intermetallic hydride design principle from limited data via interpretable machine learning

HIGHLIGHTS

• who: Matthew Witman et al. from the Livermore, California, United States University of have published the research work: Extracting an Empirical Intermetallic Hydride Design Principle from Limited Data via Interpretable Machine Learning, in the Journal: (JOURNAL)
• what: The authors demonstrate how feature importance, as identified by a gradient boosting tree regressor, uncovers the strong dependence of the metal hydride equilibrium H2 pressure on a volume-based descriptor that can be computed from just the elemental composition of the intermetallic alloy. The authors can rationalize such trends as indicators for increasing hydrogen absorption strength or decreased . . .
  

   

  

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