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使用深度学习构建定制的热力学
Xiaoli Chen1,2, Beatrice W Soh3, Zi-En Ooi3
1Department of Mathematics, National University of Singapore, Singapore, Singapore.
Nature computational science
|January 10, 2024
概括
人工智能通过从微观数据中学习宏观动态来自动化科学发现,使用一个通用的Onsager原则. 这个平台确定了诸如聚合物拉伸等复杂系统的关键热力学坐标和动态.
科学领域:
- 计算物理 计算物理
- 科学中的人工智能.
- 聚合物物理 聚合物物理
背景情况:
- 传统的物理直觉与复杂的现象作斗争.
- 自动化科学发现可以通过分析庞大的数据集来加速研究.
- 像对称性和保存定律这样的物理原理是关键的约束.
研究的目的:
- 开发一个平台,用于在随机散射系统中自动发现宏观动态.
- 从微观轨迹直接学习可解释的热力学坐标及其相关动力学.
- 应用和验证聚合物链拉伸问题的方法.
主要方法:
- 在学习系统动态中使用了泛化的Onsager原理.
- 开发了一个平台来处理微观轨迹数据.
- 同时构建了减少的热力学坐标,并解释了它们的动力学.
主要成果:
- 成功学习了聚合物拉伸的三个可解释的热力学坐标.
- 构建了聚合物拉伸的动态景观,识别了稳定和过渡状态.
- 使用已学习的动力学,证明了对聚合物拉伸率的控制.
结论:
- 开发的AI平台有效地自动化了从微观数据中发现宏观动态的发现.
- 一般化的Onsager原则为学习可解释的缩小坐标提供了一个强大的框架.
- 该方法在各种科学和技术领域具有广泛的适用性.
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