Molecular Descriptors in Matrix Completion Methods

矩阵补全方法中的分子描述符

• 批准号: 497201843
• 负责人: Professor Dr.-Ing. Hans Hasse
• 金额: --
• 依托单位: Lehrstuhl für Thermodynamik (LTD)
• 依托单位国家: 德国
• 项目类别: Priority Programmes
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/497201843?language=en
• 关键词: Molecular; Descriptors; Matrix; Completion; Methods

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.

在化学和化学工程中，混合物性质的知识是最重要的。我们最近从机器学习中引入了矩阵补全方法（mcm）作为一种预测混合物性质的新技术。这些mcm也为根据分子组分在混合物中的行为来表征分子组分开辟了新的途径。MCM背后的思想是将二进制混合物的某种性质的可用数据集存储在矩阵中，其中的行和列对应，例如溶质和溶剂。缺乏实验数据通常导致矩阵稀疏占用；然后MCM预测剩余条目，这具有很高的实用价值。因此，MCM通常比现有的物理基准测试方法产生更好的结果。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，我们将详细探讨从MCM中获得的这些潜在成分特征。特别是，我们感兴趣的是找出它们与已建立的分子成分描述符之间的关系。一方面，这将使我们能够通过开发混合mcm进一步大大提高对混合物性能的预测。另一方面，我们的目标是建立潜在成分特征作为一类新的纯成分描述符，它可以灵活地从混合性质中确定，并且可以在许多方面使用，也超出了MCM。我们的研究将有助于SPP 2363的“机器学习分子表征的设计和评估”研究领域，我们将在此基础上增加工程，机器学习和视觉数据分析方面的专业知识，同时受益于化学方面的鼓舞人心的投入。