TL;DR
This study combines first-principles calculations, machine learning, and causal analysis to understand and predict cation ordering in double perovskite oxides with high accuracy, revealing key structural and charge features influencing ordering patterns.
Contribution
It introduces a novel integration of ML classification and causal modeling to elucidate the origins of cation ordering in double perovskites, highlighting structural modes and charge differences as critical factors.
Findings
ML models predict cation ordering with ~98% accuracy
Structural modes are key features for classifying ordering types
Charge difference between A and A' ions influences layered ordering
Abstract
This work investigates the origins of cation ordering of double perovskites using first-principles theory computations combined with machine learning (ML) and causal relations. We have considered various oxidation states of A, A', B, and B' from the family of transition metal ions to construct a diverse compositional space. A conventional framework employing traditional ML classification algorithms such as Random Forest (RF) coupled with appropriate features including geometry-driven and key structural modes leads to highly accurate prediction (~98%) of A-site cation ordering. We have evaluated the accuracy of ML models by entailing analyses of decision paths, assignments of probabilistic confidence bound, and finally introducing a direct non-Gaussian acyclic structural equation model to investigate causality. Our study suggests that the structural modes are the most important features…
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