Elucidating an Atmospheric Brown Carbon Species—Toward Supplanting Chemical Intuition with Exhaustive Enumeration and Machine Learning
Elucidating an Atmospheric Brown Carbon Species—Toward Supplanting Chemical Intuition with Exhaustive Enumeration and Machine Learning
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阐明大气棕色碳物种——以详尽的枚举和机器学习取代化学直觉
DOI：
10.1021/acs.est.1c00885
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发表时间：
2021
影响因子：
11.4
通讯作者：
von Lilienfeld, O. Anatole
中科院分区：
文献类型：
--
作者：
Tapavicza, Enrico;von Rudorff, Guido Falk;De Haan, David O.;Contin, Mario;George, Christian;Riva, Matthieu;von Lilienfeld, O. Anatole
棕色碳（BrC）参与大气光吸收和气候强迫，可能对健康造成不利影响。了解BrC的形成机制和分子结构对于制定控制其环境和健康影响的战略至关重要。由于缺乏提供分子指纹的实验和具有相同质量的分子候选物的绝对数量，BrC的结构测定具有挑战性。由于固有的偏见，基于化学直觉的建议很容易出现错误。我们提出了一个无偏的算法，使用基于图形的分子生成和机器学习，它可以识别所有的分子结构的化合物参与生物质燃烧和BrC的组成。我们将此算法应用于C12 H12 O 7，一个光吸收的“测试案例”的分子，确定在室内实验的水溶液光氧化的姜酚，木材烟雾中的一个普遍的标记。在2.6亿个分子图中，该算法仅留下36，518个（0.01%）作为匹配光谱的可行候选者。虽然没有得到唯一的分子结构，只有一个化学式和紫外/维斯吸收光谱，我们讨论了进一步的还原策略和它们的功效。通过额外的数据，该方法可以更快速地识别从实验室和现场气溶胶颗粒中提取的异构体，而不会引入人为偏见。
Brown carbon (BrC) is involved in atmospheric light absorption and climate forcing and can cause adverse health effects. Understanding the formation mechanisms and molecular structure of BrC is of key importance in developing strategies to control its environment and health impact. Structure determination of BrC is challenging, due to the lack of experiments providing molecular fingerprints and the sheer number of molecular candidates with identical mass. Suggestions based on chemical intuition are prone to errors due to the inherent bias. We present an unbiased algorithm, using graph-based molecule generation and machine learning, which can identify all molecular structures of compounds involved in biomass burning and the composition of BrC. We apply this algorithm to C12H12O7, a light-absorbing “test case” molecule identified in chamber experiments on the aqueous photo-oxidation of syringol, a prevalent marker in wood smoke. Of the 260 million molecular graphs, the algorithm leaves only 36,518 (0.01%) as viable candidates matching the spectrum. Although no unique molecular structure is obtained from only a chemical formula and a UV/vis absorption spectrum, we discuss further reduction strategies and their efficacy. With additional data, the method can potentially more rapidly identify isomers extracted from lab and field aerosol particles without introducing human bias.