ION-Logic: Neural Ion-Kinetic Intelligence for Electrochemical Flow Prediction and Redox Dynamics Control

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Authors: Samir Baladi
Cited by: 1

Abstract

ION-Logic is a physics-informed AI framework for real-time prediction and optimization of ion transport dynamics in complex electrochemical and biological ion-conducting environments. The framework introduces the Lambda-Flow Index (LFI) — a six-descriptor weighted composite integrating Neural Ion-Flux Path (NIFP), Debye-Hückel Coupling Tensor (DHCT), Redox Kinetic Tensor (RKT), Membrane Selectivity Coefficient (MSC), Ion Concentration Fractal Dimension (ICFD), and Noise-Transport Inhibition Index (NTII). Validated across 42 experimental platforms spanning 5,148 Ion Transport Units (ITUs) over an 8-year program (2017–2025). LFI achieves 93.1% accuracy in predicting ionic coherence collapse with a 38-day mean early warning before macroscopic conductivity failure. Submitted to Journal of Chemical Information and Modeling (ACS), April 2026. DOI: 10.5281/zenodo.19702569. Principal Investigator: Samir Baladi, Ronin Institute / Rite of Renaissance. ORCID: 0009-0003-8903-0029.

Fields of Study & Subjects

Advanced battery technologies research Electrochemical Analysis and Applications Fuel Cells and Related Materials

Publication details

Journal / source
Zenodo (CERN European Organization for Nuclear Research)
Publication date
23 April 2026
Language
EN