acePredicting multi-heavy-metal adsorption on bimetallic zeolitic imidazolate frameworks via interpretable machine learning: From latin hypercube design to web implementation
Efficient remediation of heavy metal-contaminated water requires systematic evaluation of adsorbent performance under complex environmental conditions. In this study, an interpretable machine learning approach was applied to predict multi-metal adsorption on bimetallic zeolitic imidazolate frameworks (ZIFs) and to analyze the contributions of material properties, aqueous chemistry, and metal ion characteristics. Three structurally distinct bimetallic ZIF-derived materials were synthesized and evaluated via adsor…
A grid-search-tuned neural network predicted heavy-metal adsorption capacity on three bimetallic ZIF materials with high accuracy and exposed which factors drive uptake, supporting more targeted adsorbent design.
On independent validation data the web platform had a mean absolute percentage error of 18 %, noticeably looser than the R2 of 0.98, and the data cover only three bimetallic ZIF-derived materials.
Evidence
- Peer-reviewedJournal of Environmental Sciences2026-03-07
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Truvace Impact Record TRV-2026-1628, v1: “Predicting multi-heavy-metal adsorption on bimetallic zeolitic imidazolate frameworks via interpretable machine learning: From latin hypercube design to web implementation.” Truvace, 2026-10-16. /record/TRV-2026-1628 (accessed at citation time). sha256 9ebb6088426d3ea6…
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