This arXiv preprint (not peer-reviewed) from May 2026 employs quantum field theory for long-range electron correlations alongside machine learning force fields to probe interatomic forces in systems of hundreds of atoms. Unlike empirical models assuming uniform decay, the work shows average interaction strength follows polynomial decay while scatter remains high and anisotropic, intensifying with molecular size. Methodology relies entirely on computational benchmarks without experimental validation, a key limitation that leaves real-world transferability untested. This shifts focus from pairwise atoms to localized 'hotspots' that may dictate biomolecular folding pathways, a nuance missed in original coverage emphasizing only computational novelty. Synthesizing with Behler's foundational neural network potentials (Phys. Rev. Lett. 2007) and recent many-body MLFF benchmarks (e.g., J. Chem. Phys. 2023 on polymer systems), the findings explain MLFF superiority over classical fields in capturing emergent properties like material strength and reactivity. Traditional chemistry education overlooks these quantum many-body effects, yet they underpin everyday phenomena from protein stability to polymer elasticity.