标题： 发展和应用$Δ$-机器学习的原子间势能到铂-纳斐翁界面 显示英文标题

标题： Development and application of $Δ$-machine-learned interatomic potentials to platinum-Nafion interfaces

摘要： 机器学习的原子间势（MLPs）近年来在精度、速度和数据效率方面取得了迅速进展。然而，在多组分系统中训练稳健的MLPs仍然是一个挑战。在本工作中，我们演示了一种通过在Nafion的Dreiding势中的键合项上进行$\Delta$学习来物理引导MLP的方法。该$\Delta$-MLP尊重聚合物的内部键，但除此之外，它从密度泛函理论训练数据中学习原子相互作用和反应性。我们使用这个$\Delta$-MLP研究铂-Nafion界面的性质，分析聚合物结构、接近和远离铂催化剂的质子迁移率以及反应路径性能。该方法在处理多组分系统的数据高效性方面显示出前景，但在输入数据质量方面还需要进一步改进，以实现对聚合物-催化剂界面的结构、传输和反应性的综合洞察。 显示英文摘要

摘要： Machine-learned interatomic potentials (MLPs) have rapidly progressed in accuracy, speed, and data efficiency in recent years. However, training robust MLPs in multicomponent systems still remains a challenge. In this work, we demonstrate a method to physically inform a MLP by $\Delta$-learning the correction to the bonding term in a Dreiding potential for Nafion. The $\Delta$-MLP respects the internal bonds of the polymer but otherwise learns atomic interactions and reactivity from density functional theory training data. We use this $\Delta$-MLP to study the properties of the platinum-Nafion interface, analyzing polymer structure, proton mobility near and far from the platinum catalyst, and reaction pathway performance. The method shows promise in terms of data-efficient treatment of multicomponent systems, but further refinement is needed in the quality of input data to achieve combined insights into structure, transport, and reactivity at polymer-catalyst interfaces.