Data_Sheet_2_Computational Infrared Spectroscopy of 958 Phosphorus-Bearing Molecules.csv

Phosphine is now well-established as a biosignature, which has risen to prominence with its recent tentative detection on Venus. To follow up this discovery and related future exoplanet biosignature detections, it is important to spectroscopically detect the presence of phosphorus-bearing atmospheric molecules that could be involved in the chemical networks producing, destroying or reacting with phosphine. We start by enumerating phosphorus-bearing molecules (P-molecules) that could potentially be detected spectroscopically in planetary atmospheres and collecting all available spectral data. Gaseous P-molecules are rare, with speciation information scarce. Very few molecules have high accuracy spectral data from experiment or theory; instead, the best current spectral data was obtained using a high-throughput computational algorithm, RASCALL, relying on functional group theory to efficiently produce approximate spectral data for arbitrary molecules based on their component functional groups. Here, we present a high-throughput approach utilizing established computational quantum chemistry methods (CQC) to produce a database of approximate infrared spectra for 958 P-molecules. These data are of interest for astronomy and astrochemistry (importantly identifying potential ambiguities in molecular assignments), improving RASCALL's underlying data, big data spectral analysis and future machine learning applications. However, this data will probably not be sufficiently accurate for secure experimental detections of specific molecules within complex gaseous mixtures in laboratory or astronomy settings. We chose the strongly performing harmonic ωB97X-D/def2-SVPD model chemistry for all molecules and test the more sophisticated and time-consuming GVPT2 anharmonic model chemistry for 250 smaller molecules. Limitations to our automated approach, particularly for the less robust GVPT2 method, are considered along with pathways to future improvements. Our CQC calculations significantly improve on existing RASCALL data by providing quantitative intensities, new data in the fingerprint region (crucial for molecular identification) and higher frequency regions (overtones, combination bands), and improved data for fundamental transitions based on the specific chemical environment. As the spectroscopy of most P-molecules have never been studied outside RASCALL and this approach, the new data in this paper is the most accurate spectral data available for most P-molecules and represent a significant advance in the understanding of the spectroscopic behavior of these molecules.磷化氢（phosphine）如今已被公认是一种生物标志物，其近期在金星上的疑似探测发现使其备受关注。为跟进这一发现以及未来系外行星生物标志物探测相关工作，通过光谱法检测可能参与磷化氢生成、降解或反应的含磷大气分子的存在至关重要。我们首先枚举了可在行星大气中通过光谱法潜在探测到的含磷分子（P-molecules），并收集了所有可用的光谱数据。气态含磷分子较为稀少，相关物种信息也较为匮乏。仅有极少数分子拥有来自实验或理论的高精度光谱数据；当前最优的光谱数据则是通过高通量计算算法RASCALL获得的——该算法依托官能团理论，可根据任意分子的组成官能团高效生成近似光谱数据。本研究提出了一种依托成熟计算量子化学（computational quantum chemistry, CQC）的高通量方法，可为958个含磷分子构建近似红外光谱数据库。这些数据可应用于天文学与天体化学研究（尤其有助于识别分子归属中的潜在歧义），可用于优化RASCALL的基础数据、开展大数据光谱分析以及未来的机器学习应用。不过，对于实验室或天文场景中复杂气态混合物内特定分子的确定性实验检测而言，该数据的精度可能尚不满足要求。我们为所有分子选用了性能优异的谐振ωB97X-D/def2-SVPD模型化学方法，并针对250个较小分子测试了精度更高但耗时更长的GVPT2非谐振模型化学方法。我们分析了该自动化方法的局限性（尤其是针对鲁棒性较差的GVPT2方法），并探讨了未来的改进路径。我们的CQC计算通过提供定量强度数据、指纹区（对分子识别至关重要）与高频区（泛频、组合频）的全新数据，以及基于特定化学环境优化的基频跃迁数据，大幅改进了现有的RASCALL数据集。由于绝大多数含磷分子的光谱特性此前仅通过RASCALL及本研究提出的方法得到过研究，本文中的全新数据为绝大多数含磷分子提供了目前最为精确的光谱数据，标志着我们对这类分子光谱行为的认知取得了重大进展。