# Reactive Transport Modeling with Physics-Informed Machine Learning for Critical Minerals Applications

Source: [Reactive Transport Modeling with Physics-Informed Machine Learning for Critical Minerals Applications](<https://www.sota2.com/research/paper/68fa5e301e209131ce9e012e>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2026-03-13T05:00:04.336Z

Authors: K. Adhikari, Md. Lal Mamud, M. K. Mudunuru, K. B. Nakshatrala

Published: 2025-06-19T02:09:57.000Z

## Abstract

This study presents a physics-informed neural network (PINN) framework for reactive transport modeling for simulating fast bimolecular reactions in porous media. Accurate characterization of chemical interactions and product formation in surface and subsurface environments is essential for advancing critical mineral extraction and related geoscience applications.

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2506.15960>)

[PDF](<https://arxiv.org/pdf/2506.15960.pdf>)