Closed-Loop Inverse Design of Functional Polycyclic Lattices: A Physics-Aware Generative AI and Adaptive Control Framework in the Chrysene Tensor Space

Abstract

The acceleration of functional materials discovery is historically constrained by a reliance on unguided stochastic searches and continuous expected-variance (H2) optimizations within Euclidean probability spaces. In this work, we collaboratively introduce a physics-aware generative AI compiler that transcends these thermodynamic bottlenecks by mapping molecular synthesis into the discrete, non-commutative geometry of the Chrysene Tensor Space. By formalizing the hyper-astronomical phase space of polycyclic aromatic hydrocarbons as a bounded optimal transport problem governed by Wasserstein gradient flows, we transition inverse materials design from heuristic approximation to rigorous algebraic topology. 

To guarantee physical synthesizability and geometric stability, the generative network operates under a strict H-infinity Minimax robust control policy. This control architecture dynamically bounds the AI's predicted structural strain strictly below the covalent Dirichlet singularity, ensuring that unviable molecular topologies are autonomically excised. We demonstrate the application of this mathematical framework to the exact inverse design of solid-state charge conduits and light-harvesting arrays. By leveraging the intrinsic dual-channel quantum orthogonality of the Ic lattice and deploying Stark-tuned topological decoherence suppression, the compiler deterministically generates macrocyclic topologies that are mathematically guaranteed to preserve quantum phase coherence at ambient temperatures. Ultimately, this framework provides a verifiable, deterministic mathematical foundation for the engineering of next-generation clean energy and advanced photonic systems.