利用机器学习探索星际化学相关尘埃颗粒表面H2O的结合能分布
Machine learning exploration of binding energy distributions of H2O at astrochemically relevant dust grain surfaces
中文总结 AI 辅助
本研究利用机器学习建模，探索了尘埃颗粒表面水分子的结合能分布，揭示了不同表面性质对冰形貌和结合能的影响，为星化学模型提供了新的物理输入。
中文摘要 AI 辅助
吸附质在星际尘埃颗粒上的结合能（BEs）对吸附、脱附、扩散和表面反应至关重要，从而强烈影响恒星和行星形成区域的星化学模型。尽管最近的计算研究越来越多地报告完整的BEs分布而不是单一代表值，但这些分布通常仅针对裸露颗粒表面或厚水冰外壳。在本工作中，我们通过系统研究水在部分和完全覆盖冰的尘埃颗粒表面的BEs分布， bridging 这些区域。我们采用基于图神经网络的机器学习互原子势（MLIPs）来建模水在石墨烯和镁终止（010）面的橄榄石表面上的吸附，分别代表碳质和硅酸盐颗粒。这些模型使我们能够对在热处理（结晶）和低温（非晶）生长条件下生成的水簇、单层和双层的吸附位点进行广泛采样。在亚单层覆盖度下，底层颗粒的化学性质强烈影响冰形貌和结合能，其中硅酸盐表面的Mg-O相互作用产生特别深的结合位点。从单层覆盖度起，两种基底上的吸附均受冰内氢键主导，减少了颗粒材料的影响。在所有覆盖度范围内，非晶冰结构系统地使BEs分布向更强的结合偏移，引入了高度稳定的缺陷和口袋位点。这些结果表明，在亚单层到几层冰的范围内，BEs分布是宽泛且高度表面依赖的，并为下一代纳入表面异质性的星化学模型提供了物理上合理的输入。
英文摘要
Binding energies (BEs) of adsorbates on interstellar dust grains critically control adsorption, desorption, diffusion, and surface reactivity, and therefore strongly influence astrochemical models of star- and planet-forming regions. While recent computational studies increasingly report full distributions of BEs rather than single representative values, these distributions are typically derived for either bare grain surfaces or thick water-ice mantles. In this work, we bridge these regimes by systematically investigating the BE distributions of water on partially and fully ice-covered dust grain surfaces. We employ machine-learning interatomic potentials (MLIPs) based on graph neural networks to model water adsorption on graphene and on the Mg-terminated (010) surface of forsterite, representing carbonaceous and silicate grains, respectively. The models enable extensive sampling of adsorption sites on water clusters, monolayers, and bilayers generated under both crystalline (thermally processed) and amorphous (low-temperature) growth conditions. At submonolayer coverage, the chemical nature of the underlying grain strongly affects both ice morphology and binding energies, with Mg-O interactions on silicate surfaces producing particularly deep binding sites. From monolayer coverage onward, adsorption on both substrates is dominated by hydrogen bonding within the ice, reducing the influence of the grain material. Across all coverages, amorphous ice structures systematically shift the BE distributions toward stronger binding compared to crystalline ice, introducing highly stable defect and pocket sites. These results demonstrate that BE distributions in the submonolayer to few-layer ice regime are broad and highly surface dependent, and they provide physically motivated input for next-generation astrochemical models incorporating surface heterogeneity.
