使用在结构数据上训练的神经网络进行粗粒度建模.
Mikhail Ivanov1, Maksim Posysoev1, Alexander P Lyubartsev1
1Department of Materials and Environmental Chemistry, Stockholm University, SE-106 91 Stockholm, Sweden.
Journal of chemical theory and computation
|September 15, 2023
概括
我们开发了一种新的自下而上的粗粒法,使用在原子模拟上训练的人工神经网络. 这种方法为复杂的解决方案创造了可转移的粗粒度潜力,提高了模拟效率和准确性.
科学领域:
- 计算化学的计算化学
- 材料科学 材料科学 材料科学
- 统计力学 统计力学
背景情况:
- 粗粒化简化了复杂的分子系统,用于大规模的模拟.
- 从原子数据中开发准确的粗粒度潜力仍然是一个挑战.
- 现有的方法往往缺乏跨不同条件的可转移性.
研究的目的:
- 引入一种新的自下而上粗粒法.
- 在原子模拟数据上训练人工神经网络 (ANN),用于粗粒度交互.
- 为了证明ANN衍生潜力的可转移性.
主要方法:
- 使用了自下而上的粗粒度方法.
- 雇佣的ANN从原子模拟中受过辐射分布函数的培训.
- 应用了反向蒙特卡洛方法,将ANN权重与结构性质联系起来.
- 在Lennard-Jones系统和甲醇水溶液上进行了测试.
主要成果:
- 成功模拟了一个莱纳德-斯系统与线性ANN.
- 使用原子数据开发了甲醇-水溶液的非线性ANN.
- 证明ANN潜力可以在各种甲醇度中转移.
- 展示了即使在培训数据中不存在的度的可转移性.
结论:
- 拟议的基于ANN的粗粒法有效地产生了可转移的潜力.
- 这种方法提高了粗粒度模型的准确性和适用性.
- 在粗的模拟中,ANN提供了一个强大的工具来导出准确的站点间潜力.
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