Microscopic contributions to the deviation from Amontons friction law

This project is a highly specialized scientific research repository associated with a specific arXiv paper (2604.06890v1). Its primary value lies in the domain-specific application of Machine Learning Force Fields (MLFF) to study nanotribology in Transition Metal Dichalcogenides (TMDs). While the 5 forks within 2 days suggest active collaborative interest or lab-group distribution, the project serves as a reference implementation for scientific reproducibility rather than a commercial software product. From a competitive intelligence perspective, it has zero defensibility as a 'product' because it is a set of scripts for a specific physical experiment. However, it represents the 'frontier' of computational materials science where ML replaces traditional empirical potentials. Frontier labs (OpenAI, etc.) have no incentive to compete here as the market is restricted to academic and specialized industrial R&D (e.g., semiconductor manufacturing, lubricant design). The 'displacement horizon' is long not because the code is irreplaceable, but because scientific findings remain relevant until superseded by more accurate physical models or experimental data.