Development of coarse-grained force field for alcohols: an efficient meta-multilinear interpolation parameterization algorithm
文献情報
Junjie Song, Ying Yang, Lianghui Gao
Coarse-grained (CG) molecular dynamics are powerful tools to access a mesoscopic phenomenon and simultaneously record microscopic details, but currently the CG force fields (FFs) are still limited by low parameterization efficiency and poor accuracy especially for polar molecules. In this work, we developed a Meta-Multilinear Interpolation Parameterization (Meta-MIP) algorithm to optimize the CG FFs for alcohols. This algorithm significantly boosts parameterization efficiency by constructing on-the-fly local databases to cover the global optimal parameterization path. In specific, an alcohol molecule is mapped to a heterologous model composed of an OH bead and a hydrocarbon portion which consists of alkane beads representing two to four carbon atoms. Non-bonded potentials are described by soft Morse functions that have no tail-corrections but can still retain good continuities at truncation distance. Nearly all of the properties in terms of density, heat of vaporization, surface tension, and solvation free energy for alcohols predicted by the current FFs deviate from experimental values by less than 7%. This Meta-MIP algorithm can be readily applied to force field development for a wide variety of molecules or functional groups, in many situations including but not limited to CG FFs.
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Physical Chemistry Chemical Physics

Physical Chemistry Chemical Physics (PCCP) is an international journal co-owned by 19 physical chemistry and physics societies from around the world. This journal publishes original, cutting-edge research in physical chemistry, chemical physics and biophysical chemistry. To be suitable for publication in PCCP, articles must include significant innovation and/or insight into physical chemistry; this is the most important criterion that reviewers and Editors will judge against when evaluating submissions. The journal has a broad scope and welcomes contributions spanning experiment, theory, computation and data science. Topical coverage includes spectroscopy, dynamics, kinetics, statistical mechanics, thermodynamics, electrochemistry, catalysis, surface science, quantum mechanics, quantum computing and machine learning. Interdisciplinary research areas such as polymers and soft matter, materials, nanoscience, energy, surfaces/interfaces, and biophysical chemistry are welcomed if they demonstrate significant innovation and/or insight into physical chemistry. Joined experimental/theoretical studies are particularly appreciated when complementary and based on up-to-date approaches.











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