不同质流体的神经功能理论:基本原理和应用
Florian Sammüller1, Sophie Hermann1, Daniel de Las Heras1
1Theoretische Physik II, Physikalisches Institut, Universität Bayreuth, Bayreuth D-95447, Germany.
概括
本研究引入了混合机器学习和经典密度函数理论方法来预测流体结构和热力学. 该方法准确地模拟复杂的系统,优于对不均流体的现有理论.
科学领域:
- 软物质物理学 软物质物理学
- 计算化学的计算化学
- 统计力学 统计力学
背景情况:
- 经典密度函数理论 (DFT) 是研究不均质流体的强大工具.
- 密度配置文件和相关函数之间的功能地图的准确表示仍然是一个挑战.
- 机器学习 (ML) 为开发更准确的理论模型提供了新的途径.
研究的目的:
- 为确定流体平衡结构和热力学开发一种混合的经典DFT和ML方案.
- 使用深度神经网络来表示精确的功能地图.
- 为软物质系统提供准确的多尺度预测.
主要方法:
- 一个混合方案,将古典密度函数理论与深度神经网络相结合.
- 在硬球体流体的大法典蒙特卡洛模拟数据上训练神经网络.
- 实现函数式微积分来访问更高阶的相关函数和自由能量.
- 验证热诺瑟总和规则并执行自我一致的密度配置计算.
主要成果:
- 混合方案准确地确定了不均质流体的平衡结构和热力学.
- 基于神经网络的功能表现优于最先进的基本衡量标准 DFT.
- 该方法允许以近仿真微观精度进行宏观预测.
- 证明了密度配置文件的准确自相一致的计算.
结论:
- 函数的机器学习是软物质多尺度描述的有效工具.
- 开发的混合方案为传统的DFT方法提供了一个计算效率高,准确的替代方案.
- 这种方法弥合了微观模拟和宏观预测之间的差距.
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