Coarse-Grained Model of the Sodium Dodecyl Sulfate Anionic Surfactant Based on the MDPD--Martini Force Field

This project is a scientific artifact accompanying a research paper (arXiv:2604.13499). It targets a very specific niche in computational chemistry: the simulation of SDS surfactants. While the hybrid approach of combining MDPD (excellent for surface tension) with Martini (excellent for molecular interactions) is a novel and valuable methodological combination for researchers, it lacks any software 'moat.' The value resides entirely in the validated parameters and the methodology, which once published, can be easily reimplemented in standard MD engines like GROMACS or LAMMPS. With 0 stars and 3 forks (likely internal or close collaborators) only 2 days after release, it has no community momentum yet. Frontier labs like OpenAI or Google DeepMind are highly unlikely to compete in such a domain-specific area as SDS parameterization, though they may automate the discovery of such models in the future. The primary risk is displacement by a more accurate or more computationally efficient model from another academic group.