Second, it is difficult to obtain accurate and chemically specific partial charges for large polymer systems. Atomic partial charges are scalar point charges assigned to atoms or virtual sites when parameterizing molecular systems. Rather than treating electron clouds around atoms with full quantum mechanics, the electrostatic potential is approximated as being generated by point particles with fictitious fractional charges. Using such charges is a drastically faster simplification which is commonly employed in MD to calculate non-bonded electrostatic Coulomb interactions, with a tradeoff in accuracy that is usually seen as acceptable for many soft matter simulation applications. Determination of optimal charges from first principles is not, however, straightforward, and various calculation methods have been developed over the years, ranging from natural population analysis to semi-empirical fitting methods. Semi-empirical QM methods such as AM1-BCC are considered the standard for current partial charge methods in MD and provide reliable and sufficiently accurate partial charges for most applications. However, such semi-empirical methods inherit some of the scaling deficiencies of its DFT progenitors, and are limited in use to molecules smaller than a couple hundred atoms. Faster approximate methods (Gasteiger, 19 MMFF94 20 ) sacrifice accuracy for scalability. This scalability-accuracy trade-off is one of the issues which limit accurate parameterization of large polymeric molecules compared to the small molecule and biopeptide modelling ecosystems, often requiring ad hoc fragmentation procedures to try to break extended systems into manageable pieces and then stitch them together. This can work effectively for polymers with a specific, limited, set of monomers (for example, proteins or nucleic acids) but is much harder for more chemically diverse polymers.