Dimensional Analysis of Lewis Acidity: A Theoretical, Experimental, and Data Driven Approach

路易斯酸度的量纲分析：理论、实验和数据驱动的方法

• 批准号: 538774058
• 负责人: Professor Dr. Lutz Greb
• 金额: --
• 依托单位: Anorganisch-Chemisches Institut
• 依托单位国家: 德国
• 项目类别: Research Grants
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/538774058?language=en
• 关键词: Dimensional; Analysis; Lewis; Acidity; Theoretical

The concept of Lewis acids was formulated over one hundred years ago. Despite its extreme qualitative value, there is still no satisfactory model for a quantitative understanding of the "strength" of a Lewis acid. While some trends can be described by the principle of hard and soft acids, numerous exceptions point to significant weaknesses in this empirical approach. While in earlier eras, such and similar models were based on limited experimental data, modern computational chemistry or analytical methods allow the generation of affinity data on a substantially larger scale (factor > 1000). This project aims to investigate Lewis acidity by statistical tools. The starting point is the generation of a dataset with approximately 30,000 affinities. A machine learning model is generated by suitable training data sets, which can predict the affinity of a compound toward selected donors based on its two-dimensional Lewis structure as the only input. In parallel to these theoretical approaches, and to avoid losing sight of effects in the condensed phase, thermodynamic data on Lewis pair formation are obtained by isothermal calorimetry (ITC). Establishing ITC operation under inert gas conditions represents a novelty in inorganic molecular chemistry. In this way, experimental affinity data of around 500 Lewis pairs will become accessible, which can serve as a benchmark for the theoretical methods from the first subproject. Finally, the collected theoretical and experimental affinities of Lewis acids toward numerous Lewis bases are treated via dimensionality reduction and related methods. For example, with principal component analysis, a statement can be made on how many and which affinity scales are needed to reflect as much information as possible about a Lewis acid. By factor analysis, a physicochemical interpretation emerges as to which factors are significantly responsible for the affinity of a Lewis acid. What is the dimensionality of the Lewis acid problem? To our knowledge, this research represents the first "big data" approach to appraise this centuries-old concept. We anticipate critical knowledge gains with implications in catalysis, materials research, and the life sciences.

刘易斯酸的概念是在一百多年前形成的。尽管它的极端定性价值，仍然没有令人满意的模型，定量了解的“强度”的刘易斯酸。虽然一些趋势可以用硬酸和软酸的原理来描述，但许多例外情况表明这种经验方法存在重大缺陷。虽然在早期，这种和类似的模型是基于有限的实验数据，但现代计算化学或分析方法允许在更大的规模上（因子> 1000）生成亲和力数据。该项目旨在通过统计工具调查刘易斯酸度。起点是生成具有大约30，000个亲和力的数据集。通过合适的训练数据集生成机器学习模型，其可以基于其二维刘易斯结构作为唯一输入来预测化合物对所选供体的亲和力。与这些理论方法并行，并且为了避免忽视凝聚相中的效应，通过等温量热法（ITC）获得关于刘易斯对形成的热力学数据。在惰性气体条件下建立ITC操作代表了无机分子化学中的新奇。通过这种方式，大约500个刘易斯对的实验亲和力数据将变得可用，这可以作为第一个子项目的理论方法的基准。最后，收集的理论和实验的刘易斯酸对许多刘易斯碱的亲和力处理通过降维和相关的方法。例如，使用主成分分析，可以对需要多少和哪些亲和力尺度来反映关于刘易斯酸的尽可能多的信息做出陈述。通过因子分析，物理化学解释出现的因素是显着负责的刘易斯酸的亲和力。刘易斯酸问题的维数是多少？据我们所知，这项研究代表了第一个“大数据”方法来评估这个有着几个世纪历史的概念。我们预计关键的知识增益与催化，材料研究和生命科学的影响。