Molecular Descriptors in Matrix Completion Methods

混合物性质的知识是化学和化学工程中最重要的兴趣。我们最近引入了机器学习中的矩阵补全方法（MCMs），作为预测混合物性质的一种新技术。这些MCM还开辟了新的方法来表征分子组分的基础上，他们在混合物中的行为。MCM背后的想法是将关于二元混合物的某个属性的可用数据集存储在矩阵中，其中行和列对应，例如，溶质和溶剂。缺乏实验数据通常会导致稀疏占用的矩阵;然后MCM预测剩余的条目，这是很高的实际利益。因此，MCM往往产生更好的结果比既定的物理基准方法。为了解决预测任务，MCM仅基于混合数据确定纯成分的描述符（称为潜在成分特征）。在目前的工作中，我们将探讨这些潜在的组件功能从MCM中获得的详细信息。特别是，我们有兴趣找出，它们是如何与建立分子组分描述符。一方面，这将使我们能够通过开发混合MCMs来进一步增强对混合物性质的预测。另一方面，我们的目标是建立潜在的成分特征作为一类新的纯成分描述符，它可以灵活地确定从混合物的属性，可以用在许多方面，也超出了MCM。我们的研究将有助于SPP 2363的研究领域“用于机器学习的分子表示的设计和评估”，我们将增加来自工程，机器学习和可视化数据分析的专业知识，同时受益于化学的鼓舞人心的输入。

Knowledge on properties of mixtures is of paramount interest in chemistry and chemical engineering. We have recently introduced matrix completion methods (MCMs) from machine learning as a novel technique for predicting properties of mixtures. These MCMs also open up new ways for characterizing the molecular components based on their behavior in mixtures. The idea behind an MCM is to store the set of available data on a certain property of binary mixtures in a matrix, where the rows and columns correspond, e.g., to solutes and solvents. The lack of experimental data usually results in a sparsely occupied matrix; the MCM then predicts the remaining entries, which is of high practical interest. Hereby, the MCM often yields better results than established physical benchmark methods. For solving the prediction task, the MCM determines descriptors of the pure components (called latent component features) based only on the mixture data. In the present work, we will explore these latent component features obtained from the MCM in detail. In particular, we are interested in finding out, how they are related to established molecular component descriptors. On the one side, this will enable us to substantially further enhance the predictions of the mixture properties by developing hybrid MCMs. On the other side, we aim at establishing the latent component features as a new class of pure component descriptors, which can be determined flexibly from mixture properties and can be used in many ways, also beyond the MCM. Our research will contribute to the research area “design and evaluation of molecular representations for machine learning” of SPP 2363, to which we will add expertise from engineering, machine learning, and visual data analytics, while profiting from the inspiring input from chemistry.