标题： 从解决机器学习分类问题中获得可迁移的化学见解：热力学性质预测，原子组成优于库仑矩阵 显示英文标题

标题： Obtaining transferable chemical insight from solving machine-learning classification problems: Thermodynamical properties prediction, atomic composition as good as Coulomb matrix

摘要： 机器学习（ML）可用于构建代理模型，以快速预测感兴趣属性。 因此，ML可以应用于化学项目，其中通常的实验或计算技术对于一个样本可能需要数小时或数天。 通过这种方式，可以从广泛的数据库中提取最有希望的候选样本，并进行更深入的分析。 尽管它们具有广泛的应用性，但将ML方法应用于给定的化学问题可能会很具挑战性，因为必须做出大量设计决策，例如使用哪种分子描述符或训练模型的优化器。 在这里，我们提出了一种通过分类实验对给定分子问题进行有意义探索的方法。 这种概念上简单的方法能提供关于所选问题的可转移见解，并可用作估计预测难度、测试和优化分子表示以及开展更精确或更具雄心的项目的平台。 还可以获得物理化学方面的见解。 该方法通过使用多种分子描述符来预测来自公共数据库QM9 [Ramakrishnan2014] 中的分子的焓、吉布斯自由能、零点振动能量和定容热量容量进行说明，该数据库包含133,885种有机分子。 值得注意的结果是，对于我们提出的分类问题，低分辨率描述符“原子组成”[Tchagang2019] 在训练期间使用适当的优化器时，可以达到几乎与高分辨率“排序库仑矩阵”[Rupp2012,Montavon2012,Hansen2013] ($>90\%$) 相当的分类率。 显示英文摘要

摘要： Machine learning (ML) can be used to construct surrogate models for the fast prediction of a property of interest. ML can thus be applied to chemical projects, where the usual experimentation or calculation techniques can take hours or days for just one sample. In this manner, the most promising candidate samples could be extracted from an extensive database and subjected to further in-depth analysis. Despite their broad applicability, it can be challenging to apply ML methods to a given chemical problem since a multitude of design decisions must be made, such as the molecular descriptor to use or the optimizer to train the model. Here we present a methodology for the meaningful exploration of a given molecular problem through classification experiments. This conceptually simple methodology results in transferable insight on the selected problem and can be used as a platform from which prediction difficulty is estimated, molecular representations are tested and refined, and more precise or ambitious projects can be undertaken. Physicochemical insight can also be obtained. This methodology is illustrated through the use of multiple molecular descriptors for the prediction of enthalpy, Gibbs' free energy, zero-point vibrational energy, and constant-volume calorific capacity of the molecules from the public database QM9 [Ramakrishnan2014] with 133,885 organic molecules. A noteworthy result is that for the classification problem we propose, the low-resolution descriptor `atomic composition' [Tchagang2019] can reach a classification rate almost on par with the high-resolution `sorted Coulomb matrix' [Rupp2012,Montavon2012,Hansen2013] ($>90\%$), provided that an appropriate optimizer is used during training.