Proton Spin-Induced Dynamic Fields as Hidden Determinants of Electron Motion: A Machine Learning Approach

We propose a novel framework to investigate whether electron behavior within atomic systems is governed by hidden deterministic paths shaped by proton spin-induced dynamic fields. By extrapolating proton intrinsic angular momentum to a classical rotational model, we estimate rotational dynamics on the order of septillion revolutions per minute, suggesting ultra-high-frequency spacetime deformations at atomic scales [1, 5]. We hypothesize that the observed electron probability cloud arises not from fundamental randomness, but from observational limitations in resolving such fine-scale motion [2, 6]. To test this, we outline a machine learning-based approach that trains predictive models on sequential electron position snapshots, aiming to recover deterministic motion patterns hidden beneath apparent quantum uncertainty [4, 7]. Successful prediction of future electron locations with accuracy exceeding random baselines would provide evidence for an underlying ordered structure, offering new insights into the deep architecture of atomic dynamics and the potential unification of classical and quantum frameworks.