用学习动态模拟水分子，效率更高且精度相当。
Molecular Learning Dynamics
- 将粒子视为智能体，通过最小化损失函数实现运动预测
- 从CP2K仿真数据中推导出氧氢粒子的损失函数
- 基于学习的模拟比传统物理方法快得多，适合大规模分子模拟
我们应用物理-学习对偶性于分子系统，将相互作用粒子的传统物理描述，与一个对偶的学习描述相结合：每个粒子被建模为一个最小化损失函数的智能体。在经典物理框架中，运动方程由拉格朗日函数导出；而在学习框架中，相同方程由智能体损失函数驱动的学习动态产生。该损失函数依赖于描述其他所有粒子或智能体不变性质的标量量。为验证该方法，我们首先从基于CP2K的水分子仿真数据集中直接推断出氧和氢的损失函数。随后，利用这些损失函数构建了一种基于学习的水分子模拟，其精度与标准物理模拟相当，但计算效率显著更高。
原文摘要 · Abstract (English)
We apply the physics-learning duality to molecular systems by complementing the physical description of interacting particles with a dual learning description, where each particle is modeled as an agent minimizing a loss function. In the traditional physics framework, the equations of motion are derived from the Lagrangian function, while in the learning framework, the same equations emerge from learning dynamics driven by the agent loss function. The loss function depends on scalar quantities that describe invariant properties of all other agents or particles. To demonstrate this approach, we first infer the loss functions of oxygen and hydrogen directly from a dataset generated by the CP2K physics-based simulation of water molecules. We then employ the loss functions to develop a learning-based simulation of water molecules, which achieves comparable accuracy while being significantly more computationally efficient than standard physics-based simulations.
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