纳米颗粒
无定形二氧化硅
可转让性
可扩展性
材料科学
无定形固体
Python(编程语言)
计算机科学
纳米技术
分子动力学
化学工程
化学
计算化学
工程类
罗伊特
机器学习
有机化学
操作系统
作者
Andrew Z. Summers,Christopher R. Iacovella,Olivia M Cane,Peter T. Cummings,Clare McCabe
标识
DOI:10.1021/acs.jctc.8b01269
摘要
Despite the ubiquity of nanoparticles in modern materials research, computational scientists are often forced to choose between simulations featuring detailed models of only a few nanoparticles or simplified models with many nanoparticles. Herein, we present a coarse-grained model for amorphous silica nanoparticles with parameters derived via potential matching to atomistic nanoparticle data, thus enabling large-scale simulations of realistic models of silica nanoparticles. Interaction parameters are optimized to match a range of nanoparticle diameters in order to increase transferability with nanoparticle size. Analytical functions are determined such that interaction parameters can be obtained for nanoparticles with arbitrary coarse-grained fidelity. The procedure is shown to be extensible to the derivation of cross-interaction parameters between coarse-grained nanoparticles and other moieties and validated for systems of grafted nanoparticles. The optimization procedure used is available as an open-source Python package and should be readily extensible to models of non-silica nanoparticles.
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