Framework for Inverse Mapping Chemistry-Agnostic Coarse-Grained Simulation Models into Chemistry-Specific Models.

Coarse-grained (CG) models have allowed molecular simulations to access large enough time and length scales to elucidate relationships between macroscale properties and microscale molecular interactions. However, an unaddressed inverse-design problem concerns the identification of an optimal chemistry-specific (CS) molecule that the generic CG model represents. This has been addressed here by introducing new tools for automatically generating and refining the mapping of CS-molecule candidates to the constraints of a CG model, based on representative optimization criteria. With these tools, for each CS-molecule from a candidate group, the best mapping of that molecule onto the CG model is found and their fit is assessed by an objective function designed to emphasize matching key properties of the CG model. We employ this methodology to a range of CG models from small solvent molecules up to block copolymer systems to show its ability to find optimal candidates and to uncover the underlying length scale of some of the CG models. For instances where the identity of the CG model is known a priori, the methodology identifies the correct AA chemistry. For instances where the identity is unknown and a pool of candidates is provided, the method selects a chemistry that aligns well with physical intuition. The best candidate chemistry is also found to be sensitive to changes to the CG model.