预测二进制碳化合物混合物在粗粒加工时的人工动态加速,使用粗度体积和简单的平均规则
Melissa K Meinel1, Florian Müller-Plathe1
1Technische Universität Darmstadt, Eduard-Zintl-Institut für Anorganische und Physikalische Chemie and Profile Area Thermofluids and Interfaces, Technische Universität Darmstadt, Peter-Grünberg-Str. 8, D-64287 Darmstadt, Germany.
The Journal of chemical physics
|May 15, 2024
概括
粗粒度 (CG) 模型加速动态,但RoughMob量化了这一点. 对于碳化合物混合物,简单的平均值可以在没有广泛的模拟的情况下预测加速因子,从而使精确的动态性质研究成为可能.
科学领域:
- 计算化学计算化学
- 材料科学 材料科学 材料科学
- 分子动力学分子动力学
背景情况:
- 粗粒度 (CG) 分子模型可以降低计算成本,但可以人工加速动态.
- 这种加速阻碍了对动态性质的定量研究.
- RoughMob方法量化了粗粒加工过程中的几何变化及其对动态的影响.
研究的目的:
- 利用CG模型开发一种方法来预测二进制碳化合物混合物的动态性质加速.
- 为了确定简单的平均规则是否可以取代复杂的轨迹分析混合物特性.
主要方法:
- 使用了RoughMob方法及其粗度体积.
- 对于二进制混合物,应用了简单的平均规则,并使用了二进制度校正项.
- 为扩散系数计算的加速度因子.
主要成果:
- 简单的平均法则准确地预测碳化合物混合物中的二进制扩散和自我扩散系数的加速因子.
- 预测准确度与实验测量准确度相当.
- 对于定性度依赖,需要最小的模拟 (纯成分,等等分子混合物).
结论:
- 与简单的平均值相结合的RoughMob方法允许准确的先验预测二进制碳化合物混合物中的动态属性加速.
- 这大大减少了研究CG模型动态的计算负担.
- 该方法可以在没有广泛的原子模拟的情况下对动态性质进行可靠的定量分析.
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