Molecular abundances in protoplanetary disks are highly sensitive to the local physical conditions, including gas temperature, gas density, radiation field, and dust properties. Often multiple factors are intertwined, impacting the abundances of both simple and complex species. We present a new approach to understanding these chemical and physical interdependencies using machine learning. Specifically we explore the case of CO modeled under the conditions of a generic disk and build an explanatory regression model to study the dependence of CO spatial density on the gas density, gas temperature, cosmic ray ionization rate, X-ray ionization rate, and UV flux. Our findings indicate that combinations of parameters play a surprisingly powerful role in regulating CO compared to any singular physical parameter. Moreover, in general, we find the conditions in the disk are destructive toward CO. CO depletion is further enhanced in an increased cosmic ray environment and in disks with higher initial C/O ratios. These dependencies uncovered by our new approach are consistent with previous studies, which are more modeling intensive and computationally expensive. Our work thus shows that machine learning can be a powerful tool not only for creating efficient predictive models, but also for enabling a deeper understanding of complex chemical processes.

翻译：原行星盘中的分子丰度对局部物理条件（包括气体温度、气体密度、辐射场和尘埃特性）高度敏感。多种因素常常相互交织，影响简单和复杂分子的丰度。我们提出了一种利用机器学习理解这些化学与物理相互依赖关系的新方法。具体而言，我们研究了在典型盘条件下建模的CO案例，并构建了解释性回归模型，以分析CO空间密度对气体密度、气体温度、宇宙射线电离率、X射线电离率和紫外通量的依赖关系。结果表明，与任何单一物理参数相比，参数组合在调控CO方面起着出乎意料的强大作用。此外，总体而言，我们发现盘中的条件对CO具有破坏性。在宇宙射线环境增强以及初始C/O比更高的盘中，CO消耗进一步加剧。我们的新方法所揭示的这些依赖关系与以往更依赖建模且计算成本高昂的研究结果一致。因此，我们的工作表明，机器学习不仅是一种创建高效预测模型的强大工具，还能帮助我们更深入理解复杂的化学过程。