Emergent Knowledge-Driven Computational Atom Reasoning for Molecular Property Prediction

We present a novel gradient-free computational architecture where autonomous units interact through Compare-Adjust-Record (CAR) cycles to predict molecular properties from geometric features alone. Unlike deep learning approaches, our system requires no loss functions, backpropagation, or global optimization. Each unit maintains independent state representations [activation weight, validation score, feature vector] and communicates via tanh-bounded signals. The architecture features: (1) multi-perspective analysis of molecular features (global, local, uniform, diversity); (2) information diversity mechanisms that preserve low-similarity patterns; (3) emergent knowledge formation through iterative validation rather than supervised training. Evaluated on the QM9 dataset for HOMO-LUMO gap prediction, the CAR mechanism achieves MAE = 1.10 eV using only atomic types, atom counts, and inter-atomic distances—without any electronic structure information. The system accumulates 389 validated patterns through 3,000 inference iterations, demonstrating that useful chemical knowledge emerges from purely local interactions. This work establishes an alternative paradigm for scientific machine learning where intelligence emerges from interaction protocols rather than gradient optimization.