AI that screens materials for cleaning heavy metals from water, accepted at an IEEE conference

Metal-organic frameworks can pull toxic heavy metals out of water, but testing each one takes long series of experiments. Naitik compiled 289 experiments from 49 studies and trained an optimized CatBoost model to predict how much metal each material can capture. It reached an R-squared of 0.897, and SHAP analysis showed which properties matter most, matching established adsorption theory, so researchers can screen candidates in software before the lab.

FIELDAI & Environmental Chemistry

RESULTFirst-author paper accepted at an IEEE conference

VENUEIEEE, 2026

Naitik Gupta, YRI FellowIEEE

BEFORE THE FELLOWSHIP

An 11th grader at MM Public School in Ghaziabad, India interested in chemistry and clean water.

AFTER

First author of an explainable machine learning framework for water-purifying materials, accepted at an IEEE conference.

THE LEDGER

01First-author paper accepted at an IEEE conference

02Built a dataset of 289 experiments from 49 studies

03R-squared of 0.897 predicting adsorption capacity

04SHAP explanations consistent with adsorption theory

NEXT CASE STUDYAna Perez, First-author paper accepted at an IEEE conference

Every case study starts with one application.

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