Universal Audit of the Underlying Topological Logic of the Periodic Table: A Causal Network Analysis Based on the SRE Protocol

Abstract

Traditional chemical and materials research relies heavily on wet‑lab observation and quantum‑mechanical simulation, yet lacks a quantitatively audited, engineering‑reusable design tool built on the "discrete causal network" description of atomic structure. Taking the Status‑Relational Entropy Dynamics (SRE) framework as a heuristic working hypothesis rather than a physically proven axiom, this study constructs a molecular‑design pipeline: scaffold generation + Tier‑A self‑consistent audit → VHC topological compensation assessment → GROMACS all‑atom molecular‑dynamics final‑verification. In the Phase‑3 T‑P1a empirical stage, a full closed loop was completed over four benchmark molecules and four generated scaffolds: a bias calibration between micro‑vib pseudo‑MD and GROMACS 10 ns NVT real MD (Appendix C3), a corrected misattribution of Cond_norm blow‑up in the v3.1 protocol (Appendix C4; a 3051‑frame SVD sweep proved the 1e‑12 hard‑floor was never triggered and that amplification originates from the mean_norm^α_n exponential term), and the observation that 2/4 generated scaffolds achieved a modestly lower topology‑stability metric (λ₂) than their parent molecules in real MD (Δλ₂ ≈ −0.008). Based on the tool boundaries and limitations exposed by the empirical stage, this paper further proposes an integrated SRE + AI + MD design architecture: SRE performs ultra‑large‑scale pre‑screening over chemical space; an AI correction surrogate is trained to non‑linearly map micro‑vib fingerprints onto real‑MD metrics (with uncertainty estimates); and GROMACS is retained as a non‑removable final‑verification gate, forming a "pre‑screen → correct → final‑verify → incremental feedback" loop. This work explicitly delimits its deliverable scope: sample scale cannot be extended to a statistically meaningful magnitude under single‑point compute constraints, and ultimate material validation depends on chemical experiments — both lie beyond what an individual can independently complete. The paper is therefore delivered as a combination of a completed small‑scale empirical study and a forward‑looking design blueprint. It neither claims validated novel molecules nor asserts the physical truth of SRE axioms; rather, it offers a reproducible, traceable, and engineering‑feasible methodological path for molecular design. 物质设计(molecular / materials design)的核心矛盾在于:化学空间巨大,而逐分子进行高精度验证的成本极其高昂。传统的两条路径各有短板—— 实验观测:真实可靠,但通量极低、成本高,无法对大规模候选空间穷举; 量子力学模拟(DFT 等):对单个分子精度高,但同样难以覆盖超大候选集合。 在此背景下,学界普遍依赖描述符 + 机器学习做虚拟筛选。然而,这类方法在缺乏可信训练集(尤其是全新化学空间)时,其物理解释力弱、外推能力差,容易产出大量假阳性。 本研究探索第三条路径:以 SRE 框架为核心,将分子的"离散因果网络拓扑结构"作为底层描述,构建一套可解释的、启发式的、大规模初筛工具,再叠加AI 校正与成熟 MD 终审,形成一条完整的设计管线。 需要强调的是本研究的立场预设:SRE 框架在此仅作为启发式工作假设(heuristic working hypothesis)。本文不证明 SRE 公理的客观物理真理性,也不将 SRE 视为已被验证的物理定律;它扮演的角色,是一个自研的、可快速计算的分子描述与筛选工具——类似于一个统计上可能具备筛选信号、但物理机制尚存争议的虚拟筛选描述符集合。