学习分子动力学:通过机器学习模拟器预测眼镜的动力学
Han Liu1, Zijie Huang2, Samuel S Schoenholz3
1SOlids inFormaTics AI-Laboratory (SOFT-AI-Lab), College of Polymer Science and Engineering, Sichuan University, Chengdu 610065, China. happylife@ucla.edu.
Materials horizons
|June 29, 2023
概括
本研究引入了基于观察的图形网络 (OGN),以模拟复杂的玻璃动态,仅使用静态结构,绕过传统的物理定律. OGN模拟加速了原子动力学建模,同时节约了能量和动量.
科学领域:
- 计算物理学的计算物理.
- 材料科学是一种材料科学.
- 人工智能的人工智能是人工智能.
背景情况:
- 模拟原子动力学,就像玻璃动力学一样,由于复杂的物理定律和计算成本而具有挑战性.
- 现有的方法在捕捉这些动态方面努力平衡准确性和效率.
研究的目的:
- 开发一种新的框架来模拟复杂的原子动力学,特别是玻璃动力学,绕过明确的物理定律.
- 为了有效地预测动态原子行为,利用静态结构信息.
主要方法:
- 引入基于观察的图形网络 (OGN) 框架,利用图形神经网络 (GNN).
- 训练和应用OGN来预测各种原子系统中的静态结构中的原子轨迹.
- 与传统的分子动力学 (MD) 模拟进行比较.
主要成果:
- 在数百个时间步骤中,OGN成功预测了复杂的玻璃动态的原子轨迹.
- 发现原子动态在无序相的静态结构中被显著编码.
- 与MD相比,OGN模拟显示了增强的计算速度 (≥5x),节省了能量和动量.
结论:
- OGN框架为模拟多体动态提供了一个强大的,高效的替代方案,特别是在无序系统中.
- 这些发现表明,静态结构包含了关于动态过程的大量信息,使物理无关模拟成为可能.
- 这种方法有可能广泛适用于各种多体动力学问题.
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