ArticlePrecision chemistry2026
Adaptive Reduction of Multicollinearity for Machine Learning Prediction of CO Adsorption Energies on Single Atoms.
Article in Precision chemistry, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Data-driven approaches are pivotal for optimizing single-atom catalysts. However, when the training data size is limited, the prevalence of multicollinearity among physicochemical features frequently destabilizes statistical models and obscures chemical interpretation. To address this challenge, we developed a wrapper-based feature selection algorithm termed adaptive reduction of multicollinearity with retention (ARMR). This method iteratively identifies feature subsets that satisfy strict multicollinearity constraints while minimizing cross-validated prediction error. The features selected by ARMR yielded robust predictive performance in both linear and nonlinear models, clearly outperforming baselines derived from univariate correlations or standard importance rankings. The efficacy of ARMR was validated through machine learning predictions using a density functional theory dataset of CO adsorption energies on 11 transition metals embedded as a single atom on three TiO
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