Published April 23, 2026 | Version 1.0.0

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

  • 1. ROR icon Ronin Institute for Independent Scholarship 2.0
  • 2. Rite of Renaissance

Description

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.

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ION-Logic_Full_Paper.pdf

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Additional details

Related works

Is derived from
Journal article: 10.17605/OSF.IO/Y82AM (DOI)
Is documented by
Dataset: https://gitlab.com/gitdeeper11/ION-Logic (URL)
Is source of
Software: https://pypi.org/project/ion-logic-engine/1.0.0/ (URL)
Is supplement to
Publication: 10.5281/zenodo.19702569 (DOI)

Funding

U.S. National Science Foundation
Electrochemical AI for Ion Transport NSF-CHE-2026
Extreme Science and Engineering Discovery Environment
PINN HPC Allocation TG-CHE2026-ION

Software

Repository URL
https://gitlab.com/gitdeeper11/ION-Logic
Programming language
Python
Development Status
Active

References