EL-RAKHAWI ELECTRO-CHEMICAL CAUSAL INTELLIGENCE ARCHITECTURE: MOLECULAR RESERVOIR COMPUTING VIA REDOX-DRIVEN SYNAPTIC LATTICES
Abstract
The El-Rakhawi Electro-Chemical Causal Intelligence Architecture (ECCIA) bridges AI, physics, chemistry, and neuroscience by shifting to molecular physical computing. It utilizes redox-active polymer lattices to natively execute causal machine learning. Computation occurs via physical ion transport and localized redox reactions, emulating biophysical synaptic plasticity. Governed by Nernst-Planck reaction-diffusion dynamics, the system performs continuous-time causal inference without vanishing gradients. It employs Electro-Chemical Causal Reservoir Computing, satisfying the Echo State Property for mathematical robustness. The algorithm minimizes the El-Rakhawi Electro-Chemical Causal Loss (L_ECCL), strictly enforcing mass and charge conservation. Performance is quantified by the El-Rakhawi Electro-Chemical Computational Efficiency Metric (EECEM), measuring bits per Joule. This enables ultra-low-power edge AI, biocompatible neuro-prosthetics using ionic gradients, and native molecular simulations, strictly adhering to physical laws.
