Correct pressure broadening is essential for modelling radiative transfer in atmospheres, however data are lacking for the many exotic molecules expected in exoplanetary atmospheres. Here we explore modern machine learning methods to mass produce pressure broadening parameters for a large number of molecules in the ExoMol data base. To this end, state-of-the-art machine learning models are used to fit to existing, empirical air-broadening data from the HITRAN database. A computationally cheap method for large-scale production of pressure broadening parameters is developed, which is shown to be reasonably (69%) accurate for unseen active molecules. This method has been used to augment the previously insufficient ExoMol line broadening diet, providing air-broadening data for all ExoMol molecules, so that the ExoMol database has a full and more accurate treatment of line broadening. Suggestions are made for improved air-broadening parameters for species present in atmospheric databases.

中文翻译：

---

使用机器学习预测线展宽的旋转依赖性

正确的压力展宽对于模拟大气中的辐射传输至关重要，但是缺乏系外行星大气中预期的许多奇异分子的数据。在这里，我们探索现代机器学习方法，以批量生产 ExoMol 数据库中大量分子的压力展宽参数。为此，我们使用最先进的机器学习模型来拟合 HITRAN 数据库中现有的经验性空中扩展数据。开发了一种计算成本低廉的方法，用于大规模生产压力展宽参数，该方法对于看不见的活性分子来说具有合理的准确度（69%）。该方法已用于补充之前不足的ExoMol谱线展宽饮食，提供所有ExoMol分子的空气展宽数据，使ExoMol数据库对谱线展宽有完整且更准确的处理。针对大气数据库中存在的物种，提出了改进空气展宽参数的建议。