AstroBind：基于几何表面描述符预测星际水冰结合能分布的机器学习方法
AstroBind: Machine learning prediction of binding energy distributions on interstellar water ice from geometric surface descriptors
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中文总结 AI 辅助
该研究开发了名为AstroBind的机器学习模型，用27个几何描述符预测星际非晶态水冰的结合能分布，在15个冰表面上$R^2$达0.90，可快速实现天体化学模型所需的相关参数化。
中文摘要 AI 辅助
非晶态固体水上的结合能调控分子云与原行星盘中的分子脱附，但从第一性原理计算无序冰表面异质结合位点的结合能计算成本极高。本研究旨在开发一种快速、可解释的非晶态固体水结合能预测方法，无需显式电子结构计算即可捕捉表面异质性。我们利用冰-吸附质局部环境的27个几何描述符训练机器学习模型，该模型在13种吸附质在非晶态固体水团簇上的结合能数据上训练，通过15个冰表面的合并留一团簇交叉验证进行评估，并对完全未纳入训练的自由基物种评估其迁移能力。该模型在15个冰表面上的合并留一团簇交叉验证决定系数$R^2=0.90$（即模型捕获结合能方差的比例，1为完美拟合），平均绝对误差为378 K；对训练中完全排除的自由基物种的迁移性评估显示合并$R^2=0.79$，不过当未成对电子直接参与表面相互作用时，模型精度会下降。本研究结果为气体-尘埃天体化学模型中结合能及脱附/扩散速率的快速、分布感知参数化提供了途径，同时保留了异质冰表面的物理可解释性。
英文摘要
Binding energies on amorphous solid water regulate molecular desorption in molecular clouds and protoplanetary disks, but computing them from first principles across the heterogeneous binding sites of disordered ice surfaces is computationally prohibitive. We aim to develop a rapid, interpretable method for predicting binding energies on amorphous solid water that captures surface heterogeneity without requiring explicit electronic-structure calculations. We trained a machine-learning model using 27 geometric descriptors of the local ice-adsorbate environment. The model was trained on binding energies for 13 adsorbates on amorphous solid water clusters and evaluated through pooled leave-one-cluster-out validation across 15 ice surfaces. Its transferability was assessed on radical species excluded entirely from training. The model achieves a pooled leave-one-cluster-out coefficient of determination $R^2 = 0.90$ (the fraction of variance in the binding energies captured by the model, where 1 is a perfect fit), and a mean absolute error of 378 K across 15 ice surfaces. It also transfers to radical species withheld from training, with a pooled $R^2 = 0.79$, although its accuracy decreases when the unpaired electron contributes directly to the surface interaction. Our results demonstrate a route towards rapid, distribution-aware parameterisation of binding energies and desorption/diffusion rates in gas-grain astrochemical models, while preserving physical interpretability on heterogeneous ice surfaces.
