Accurate atomistic simulations of gas-surface scattering require potential energy surfaces that remain reliable over broad configurational and energetic ranges while retaining the efficiency needed for extensive trajectory sampling. Here, we develop a data-driven workflow for constructing a machine-learning interatomic potential (MLIP) tailored to gas-surface scattering dynamics, using nitric oxide (NO) scattering from highly oriented pyrolytic graphite (HOPG) as a benchmark system. Starting from an initial ab initio molecular dynamics (AIMD) dataset, local atomic environments are described by SOAP descriptors and analyzed in a reduced feature space obtained through principal component analysis. Farthest point sampling is then used to build a compact training set, and the resulting Deep Potential model is refined through a query-by-committee active-learning strategy using additional configurations extracted from molecular dynamics simulations over extended ranges of incident energies and surface temperatures. The final MLIP reproduces reference energies and forces with high fidelity and enables large-scale molecular dynamics simulations of NO scattering from graphite at a computational cost far below that of AIMD. The simulations provide detailed insight into adsorption energetics, trapping versus direct scattering probabilities, translational energy loss, angular distributions, and rotational excitation. Overall, the results reproduce the main experimental trends and demonstrate that descriptor-guided sampling combined with active learning offers an efficient and transferable strategy for constructing MLIPs for gas-surface interactions.

翻译：气体-表面散射的精确原子模拟需要势能面在广泛的构型和能量范围内保持可靠，同时具备高效轨迹采样所需的计算效率。本文以一氧化氮（NO）在高定向热解石墨（HOPG）上的散射为基准体系，开发了一种面向气体-表面散射动力学的机器学习原子间势函数（MLIP）数据驱动构建流程。从初始的第一性原理分子动力学（AIMD）数据集出发，采用SOAP描述符描述局部原子环境，并通过主成分分析在降维特征空间中进行分析。随后利用最远点采样构建紧凑训练集，借助基于查询委员会的主动学习策略，从涵盖更广入射能量和表面温度范围的分子动力学模拟中提取额外构型对所得深度势模型进行优化。最终MLIP以高保真度复现参考能量和力，使得石墨表面NO散射的大规模分子动力学模拟能够在远低于AIMD的计算成本下进行。模拟提供了对吸附能量学、俘获与直接散射概率、平动能损失、角度分布以及转动激发等物理过程的深入认识。总体而言，结果重现了主要实验趋势，表明描述符引导采样与主动学习相结合的策略为构建气体-表面相互作用的MLIP提供了高效且可迁移的方案。