标题： 从原子到动力学：在没有集体变量的情况下学习传递函数 显示英文标题

标题： From Atoms to Dynamics: Learning the Committor Without Collective Variables

摘要： 本简要通讯介绍了一种基于几何向量感知器的图神经网络架构，该架构可直接从原子坐标预测传递函数，无需依赖手工设计的协同变量(CVs)。 该方法提供了原子级别的可解释性，无需依赖先验假设即可确定复杂转变中的关键原子参与者。 在多种分子系统中应用该方法，能够准确推断传递函数，并突出显示每个重原子在转变机制中的重要性。 它还能对基础过程的速率常数进行精确估计。 所提出的方法通过实现无协同变量的学习和自动识别复杂分子过程的物理上有意义的反应坐标，为理解和建模复杂动力学开辟了新途径。 显示英文摘要

摘要： This Brief Communication introduces a graph-neural-network architecture built on geometric vector perceptrons to predict the committor function directly from atomic coordinates, bypassing the need for hand-crafted collective variables (CVs). The method offers atom-level interpretability, pinpointing the key atomic players in complex transitions without relying on prior assumptions. Applied across diverse molecular systems, the method accurately infers the committor function and highlights the importance of each heavy atom in the transition mechanism. It also yields precise estimates of the rate constants for the underlying processes. The proposed approach opens new avenues for understanding and modeling complex dynamics, by enabling CV-free learning and automated identification of physically meaningful reaction coordinates of complex molecular processes.