Authors: Kyungae Ahn
Source: Zenodo (CERN European Organization for Nuclear Research) (preprint, 2026-03-04)
Field: Materials Science — Materials Chemistry

One Pipeline, Every Field: The Structural Inevitability of Disciplinary Convergence Under AI Methodology (Part 2 of a 3-part series.) (Revised version) When any field adopts AI methodology for data-driven analysis, it passes through the same computational pipeline. This paper argues that this passage makes methodological convergence across disciplines effectively inevitable---not as a logical necessity, but because the pipeline consistently matches or exceeds domain-specific methods, leaving fields no competitive reason to resist adoption. It demonstrates this inevitability in its strongest form: across biology, chemistry, and physics---the fields with the most entrenched domain-specific methods. We introduce the Learned Interaction Pipeline (LIP)---a four-stage computational structure (tokenize, interact, aggregate, decode)---as a diagnostic lens instantiated from Battaglia et al.'s (2018) encode-process-decode framework, and show that it is not specific to any domain but inherent to all AI-based data analysis. Four independently developed models---AlphaFold (biology), MACE-MP-0 (chemistry), the General Physics Transformer and Fourier Neural Operator (physics)---each instantiate this pipeline, differing only in domain-specific implementation choices. This paper demonstrates this inevitability in three ways. First, structurally: the four models map onto the same pipeline across tokenization, interaction, aggregation, and decoding stages. Second, empirically: when a chemistry model (MACE-MP-0) and a biology model (ESM-2) process the same molecules, their internal representations converge at interaction layers and diverge at output layers---a pattern reproduced across 27 proteins spanning 10 to 164 residues (p = .0001, permutation test; p = 2.3 × 10⁻¹², paired t-test). The convergence depends on physical content: randomizing atomic coordinates (p = 1.0 × 10⁻¹³) or shuffling amino acid sequences (p = 6.6 × 10⁻¹¹) each significantly reduces cross-model similarity. Third, functionally: the chemistry model's representations predict protein residue contacts above chance---a result whose significance lies not in its magnitude but in its existence: that any above-chance signal is possible without biological training confirms that functional information is transferable across disciplinary boundaries via the shared pipeline. A critical control establishes the boundary: a general-purpose language model (GPT-2) shows no meaningful representational convergence (mean CKA 0.07 vs. 0.27 for MACE--ESM-2), confirming that pipeline sharing alone is insufficient---convergence requires training on data from the natural world. Domain-specific training does not erase this convergence but modulates it: training on chemistry data reduces cross-model similarity in 15 of 20 proteins tested, with the effect absent below 23 residues and consistently present above 28---the scale at which emergent structural properties begin to dominate. Large-scale validation (N = 88 proteins, 10--640 residues) confirms gradient persistence in 97% of cases. Bibliometric crossover points (Chemistry 2019; Biology, Physics 2023) are consistent with phase-transition rather than incremental augmentation dynamics. (v2 to v3) Version 3 — What's new LIP framing clarified: The Learned Interaction Pipeline is now explicitly positioned as a diagnostic lens instantiated from Battaglia et al. (2018), not a novel architecture claim.Two-claim structure: Structural Claim (Sections 2–3) and Empirical Claim (Section 5) are explicitly separated so each can be evaluated independently.AUC reframing: The zero-shot contact prediction result (AUC ≈ 0.58) is reframed around the existence of transfer rather than its magnitude.Five new experiments: (1) Fine-tuning ramp (zero-shot AUC = 0.864, outperforming ESM-2 at all training set sizes); (2) Gradient persistence confirmed across 19 proteins (100% positive gradient sign, Spearman r = −.91); (3) Cross-scale RSA between atomic, sequence, and continuum representations; (4) Large-scale PDB validation extended to N = 88 proteins (10–640 residues); (5) Bibliometric analysis of AI vs. domain-method crossover points (OpenAlex, 2010–2024).Augmentation vs. replacement: New paragraph in Discussion explicitly distinguishing AI augmentation from methodological replacement, with bibliometric evidence.CKA calibration note: Added context that cross-architecture CKA of 0.2–0.4 is the expected range for inter-domain comparison. Shuffling ; Pipeline (software) ; Convergence (economics) ; Field (mathematics) ; Domain (mathematical analysis) ; Iterated function ; Process (computing) ; Permutation (music) Machine Learning in Materials Science ; Bioinformatics and Genomic Networks ; Computational Drug Discovery Methods