Topological Flux Screening and Causal-AI Discovery of Persistent Superconducting Channels in Miassite (Rh17S15) and Novel Intermetallic Lattices

Abstract

Conventional superconductivity models, notably the Bardeen-Cooper-Schrieffer (BCS) theory, rely heavily on phonon-mediated electron pairing within continuous thermodynamic frameworks, often failing to explain unconventional or high-temperature superconducting anomalies in complex crystal lattices. Building upon the Entropic-Resistance framework where spacetime is modeled as a discrete, algorithmically expanding topological network, we present a recursive space-time network framework coupled with a Causal-AI "What-If" engine to analyze topological stress-mitigation and persistent current formation. Utilizing real crystallographic data retrieved from the Materials Project database via its REST API (mp-api) alongside a high-throughput screening of 1,500 candidate intermetallic structures, we demonstrate that superconductivity can be effectively modeled as an emergent topological property governed by non-commutative transport operators and localized relaxation rates (lambda_i). Our deterministic forward-sensitivity Taylor operators (j1, j2, j3) successfully extract network memory and predict novel candidate lattices (such as Rb4SbRhCl12 and Y3Rh2) that exhibit dissipationless persistent current plateaus. This shifts the paradigm of superconductor discovery from empirical trial-and-error chemistry to deterministic topological optimization. Author's Note on Methodology and AI Collaboration:The fundamental hypotheses, intuitive leaps, and core axiomatic concepts presented in this work—particularly the recursive formulation of time, its exponential relaxation and straightening mechanisms involving Euler’s number (e), and the topological network ontology—are the original, human-generated concepts of the author (László J. Németh). The structural elaboration, formal academic drafting, and textual synthesis of these ideas were collaboratively achieved with the assistance of large language models, specifically Google Gemini.