Quantitative and Qualitative Tools for Predicting the Products and Mechanisms of Chemical Reactions

预测化学反应产物和机理的定量和定性工具

• 批准号: RGPIN-2018-06652
• 负责人: Ayers, Paul
• 金额: $ 8.81万
• 依托单位: McMaster University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=644354
• 关键词: Quantitative; Qualitative; Tools; Predicting; Products

Chemists solve problems by making molecules and materials with useful properties. Sometimes a chemical problem can be solved by finding an inexpensive and environmentally friendly way to manufacture a known substance. Computational chemistry software allows chemists to explore possible synthetic pathways before embarking on the time-consuming quest to make a molecule. Sometimes solving a chemical problem requires creating an entirely new substance. It is impossible to systematically test all possible substances, so chemists use qualitative theoretical rules and quantitative computations to navigate through the astronomical number of possibilities until they find a substance with desirable properties. ******I develop theoretical models and computational software for predicting chemical reaction pathways, describing chemical phenomena, and designing molecules with desirable properties. At the most fundamental level, I develop new computational methods to describe how electrons bind atoms into molecules. In conventional approaches to this problem, electrons are assumed to move quasi-independently, so that a single electron configuration can be clearly identified. This approximation is unsuitable for exploring chemical reactivity because it is unreliable for strained molecular geometries (chemical transition states, stretched bonds, etc.). Because traditional multiconfiguration methods are computationally expensive and relatively difficult to use, my group is developing an alternative approach, which shares some of the features (computational speed, ease-of-use) with single-configuration methods, but which is not biased towards any specific electron configuration. Our method will allow us to reliably predict the energy of molecular structures and, thereby, predict how chemical reactions occur. My group also develops conceptual tools that predict the most reactive parts of a molecule and allow us to understand why some reactions occur readily, while others do not. These insights lead to intuitive rules that chemists can use to guide the search for new phenomena. These tools can also be used by machine-learning methods, where a computer (rather than a human chemist) gets data about the features and properties of known molecules, discovers rules for organizing this data, and uses these rules to predict new molecules with desirable properties. These methods are disseminated as free and open-source software, usually as part of the HORTON package.

化学家通过制造具有有用性质的分子和材料来解决问题。有时，化学问题可以通过找到一种廉价且环保的方法来制造已知物质来解决。计算化学软件允许化学家在开始耗时的分子合成之前探索可能的合成途径。有时解决化学问题需要创造一种全新的物质。系统地测试所有可能的物质是不可能的，因此化学家使用定性理论规则和定量计算来导航天文数字的可能性，直到他们找到一种具有理想特性的物质。** 我开发理论模型和计算软件，用于预测化学反应途径，描述化学现象，并设计具有理想特性的分子。在最基本的层面上，我开发了新的计算方法来描述电子如何将原子结合成分子。在解决这个问题的传统方法中，电子被假定为准独立运动，这样就可以清楚地识别出单个电子的构型。这种近似不适合探索化学反应性，因为它对应变分子几何形状（化学过渡态，拉伸键等）不可靠。由于传统的多组态方法计算成本高，相对难以使用，我的团队正在开发一种替代方法，它与单组态方法共享一些功能（计算速度，易用性），但不偏向于任何特定的电子组态。我们的方法将使我们能够可靠地预测分子结构的能量，从而预测化学反应如何发生。我的团队还开发了概念工具，可以预测分子中最具反应性的部分，并使我们能够理解为什么有些反应容易发生，而另一些则不会。这些见解导致了化学家可以用来指导寻找新现象的直观规则。这些工具也可以用于机器学习方法，其中计算机（而不是人类化学家）获得有关已知分子的特征和性质的数据，发现组织这些数据的规则，并使用这些规则来预测具有理想性质的新分子。这些方法作为自由和开放源码软件传播，通常作为HORTON软件包的一部分。