Hierarchical cross-scale machine learning for enhanced interpretation and prediction of phosphorus removal by metal oxides materials.
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| Title: | Hierarchical cross-scale machine learning for enhanced interpretation and prediction of phosphorus removal by metal oxides materials. |
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| Authors: | Wei, Yuchen1 (AUTHOR), Xu, Shiyun1 (AUTHOR), Xu, Wenwen1 (AUTHOR), Zhuang, Guanju1 (AUTHOR), Zhang, Yanyang1,2 (AUTHOR) zhangyanyang@nju.edu.cn, Yang, Zhichao1,2 (AUTHOR), Shan, Chao1,2 (AUTHOR), Zhang, Weiming1,2 (AUTHOR), Pan, Bingcai1,2 (AUTHOR) |
| Source: | Environmental Research. Mar2026, Vol. 294, pN.PAG-N.PAG. 1p. |
| Subjects: | Machine learning, Multiscale modeling, Water pollution remediation, Phosphate removal (Sewage purification), Prediction models, Nanostructures, Metallic oxides |
| Abstract: | Phosphorus pollution necessitates advanced water remediation technologies. Metal-oxide materials show significant promise but face complexity arising from interdependent multiscale factors—spanning molecular speciation, material nanostructure, and operational conditions. Conventional machine learning (ML) approaches may suffer from interpretability bias, where macroscopic features disproportionately dominate predictions, obscuring critical nano/molecular-scale mechanisms. To overcome this limitation, we introduce a cross-scale hierarchical ML framework that integrates a mechanism-aware descriptor—the Phosphorus Selectivity Index (PSI)—to explicitly bridge molecular/nanoscale information with operational-scale kinetics. PSI quantitatively captures structure–reactivity relationships between phosphorus species and metal oxide active sites and correlates with DFT adsorption energies (Pearson R = 0.72). Embedding PSI corrects biased interpretation from our basic multi-scale model and improves prediction of phosphorus removal kinetics (log(k)). The cross-scale hierarchical model (CSO model) significantly outperformed the basic multi-scale model (MSO), achieving superior test-set accuracy (R2 = 0.77 vs. 0.69 for log(k)), elevating the mechanistic relevance of pore nanostructure, phosphorus functional groups, and quantum descriptors (i.e., E gap). Independent validations on challenging phosphonates/organophosphate esters showed lower prediction errors with the CSO model than the MSO model. This work establishes a promising cross-scale hierarchical and mechanism-aware ML framework for predictive design of phosphorus-removal materials and accurate rate forecasting, advancing the translation from microscopic insight to engineering practice for water treatment functional materials. [Display omitted] • Cross-scale hierarchical model is developed for P removal rate prediction. • A new cross-scale descriptor is built to enhance interpretation in small datasets. • The model helps bridging feature gaps from molecular to operational scale. • The model improves model prediction accuracy for independent data. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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