化学环境表象的反演
Inversion of the chemical environment representations
摘要：
用于材料设计的机器学习生成方法是通过在适当的原子特征上表示给定的化学结构（固体或分子）来构建的，所述原子特征通常称为结构描述符。这些必须完全描述系统，必须促进训练过程，并且必须是可逆的，以便可以提取对应于模型输出的原子配置。通常，最有效的结构描述符不会自动满足最后一个要求，即表示不是直接可逆的。这样的缺点严重限制了我们选择最合适的描述符的自由，从而限制了我们构建生成模型的灵活性。在这项工作中，我们提出了一个通用的优化方法，能够反转任何当地的多体描述符的化学环境，回到一个carbohydrate表示。该算法，然后实施与局部结构的双谱表示，并证明了一些分子。因此，这里提出的方案，代表了一种通用的方法来反转的结构描述符，使有效的结构生成模型的建设。
英文摘要：
Machine-learning generative methods for material design are constructed by representing a given chemical structure, either a solid or a molecule, over appropriate atomic features, generally called structural descriptors. These must be fully descriptive of the system, must facilitate the training process and must be invertible, so that one can extract the atomic configurations corresponding to the output of the model. In general, this last requirement is not automatically satisfied by the most efficient structural descriptors, namely the representation is not directly invertible. Such drawback severely limits our freedom of choice in selecting the most appropriate descriptors for the problem, and thus our flexibility to construct generative models. In this work, we present a general optimization method capable of inverting any local many-body descriptor of the chemical environment, back to a cartesian representation. The algorithm is then implemented together with the bispectrum representation of the local structure and demonstrated for a number of molecules. The scheme presented here, thus, represents a general approach to the inversion of structural descriptors, enabling the construction of efficient structural generative models.
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