Computational astrochemistry: general discussion
Computational astrochemistry: general discussion
复制标题
计算天体化学：一般讨论
DOI：
10.1039/d3fd90027d
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发表时间：
2023
影响因子：
3.4
通讯作者：
Bromley S
中科院分区：
文献类型：
--
作者：
Bromley S
Kristen Darnell开始讨论Serena Viti的论文：你讨论了使用机器学习来确定重要的结合能。你的目标是在更传统的天体化学模型中使用这些结合能，还是设想一种不同的方法？Serena Viti回答：这项研究背后的动机（https：//doi. org/10.1039/d3 fd 00007 a）的结论有两个方面：首先，结合能是控制气体-颗粒“相互作用”的关键参数;其次，文献中关于结合能的一些值存在分歧，正如我们在法拉第讨论中所看到的（也见参考文献1中的表4），明确的证据表明每个物种的结合能不只一个值。这项特殊的研究是对不同方法的探索，以限制结合能。事实上，至少从我的角度来看，一旦受到约束，我会使用更传统的天体化学模型中的结合能值（或值的范围）。
Kristen Darnell opened a discussion of the paper by Serena Viti: You discussed using machine learning to determine the binding energies that are important to consider. Is your goal to use these binding energies in more traditional astrochemical models or are you envisioning a different approach?Serena Viti replied: The motivation behind this study (https://doi. org/10.1039/d3fd00007a) was two fold: rst of all, it is clear that binding energies are key parameters that govern the gas–grain “interactions”; secondly, there are disagreements on some of the values of the binding energies in the literature and, as we have seen at this Faraday Discussion (see also Table 4 in ref. 1), clear evidence that there is not just one value of binding energy per species. This particular study was an exploration of different methodologies to constrain binding energies. Indeed, at least from my side, once constrained, I would use the values (or the range of values) of the binding energies in more traditional astrochemical models.