期刊
FRONTIERS IN CHEMISTRY
卷 10, 期 -, 页码 -出版社
FRONTIERS MEDIA SA
DOI: 10.3389/fchem.2022.982757
关键词
coarse-graining; organic semiconductors; machine learning; backmapping; structure-properity relationships
资金
- Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) [233630050-TRR 146]
In this study, the quality of different coarse-grained models is evaluated at the atomistic resolution using reverse-mapping strategies. Significant discrepancies between the all-atom and coarse-grained ensembles are revealed, and the reintroduced details allow for force computations and a clear ranking of the coarse-grained models.
The potential of mean force is an effective coarse-grained potential, which is often approximated by pairwise potentials. While the approximated potential reproduces certain distributions of the reference all-atom model with remarkable accuracy, important cross-correlations are typically not captured. In general, the quality of coarse-grained models is evaluated at the coarse-grained resolution, hindering the detection of important discrepancies between the all-atom and coarse-grained ensembles. In this work, the quality of different coarse-grained models is assessed at the atomistic resolution deploying reverse-mapping strategies. In particular, coarse-grained structures for Tris-Meta-Biphenyl-Triazine are reverse-mapped from two different sources: 1) All-atom configurations projected onto the coarse-grained resolution and 2) snapshots obtained by molecular dynamics simulations based on the coarse-grained force fields. To assess the quality of the coarse-grained models, reverse-mapped structures of both sources are compared revealing significant discrepancies between the all-atom and the coarse-grained ensembles. Specifically, the reintroduced details enable force computations based on the all-atom force field that yield a clear ranking for the quality of the different coarse-grained models.
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