Molecular Learning of a Soft-Disks Fluid

This work is based on the Equivalence between Molecular Dynamics and Neural Network. It provides learning proofs in a Lennard-Jones (LJ) fluid, presented as a network of particles having non-bonded interactions. I describe the fluid’s learning as the property of an order that emerges as an adaptation in establishing equilibrium with energy and thermal conser-vation. The experimental section demonstrates the fluid can be trained with logic-gates patterns. The work goes beyond Molecular Computing’s application, explaining how this model uses its intrinsic minimizing properties in learning and predicting outputs. Finally, it gives hints for a theory on real chemistry’s computational universality.