Beyond the Orbital Picture: New Computational Approaches to Chemical Phenomena

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，一个化学问题可以通过找到一种有效的、对环境友好的方法来解决，这种方法可以从容易获得的原料中制造出已知的物质。计算化学软件可以让化学家在着手制造一种物质之前探索可能的合成途径。有时解决一个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，所以化学家们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性质的物质。我开发了预测化学反应路径、描述化学现象和设计具有理想性质的分子的计算方法和理论模型。在最基本的层面上，我找到了新的计算方法来描述电子如何将原子结合到分子中。解决这个问题的传统方法是基于轨道图。在轨道图中，假定电子的运动是准独立的。因此，传统的方法无法描述具有重要技术意义的物质，如非传统超导体和自旋电子材料，其中的电子是强相关的。我的研究小组正在开发轨道图的替代方案。首先，我们正在开发计算分子性质的替代计算方法；这些方法可以帮助化学家确定所提出的反应路径是否可信。其次，我们正在开发用于化学洞察的无轨道工具。这些工具提供了直观的规则，化学家可以用来理解和探索新的化学现象。此外，这些工具可用于机器学习方法，即计算机(而不是人类化学家)收集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，并使用这些规则预测具有所需性质的新分子。

Chemists solve problems by making molecules and materials with useful properties. Sometimes a chemical problem can be solved by finding an efficient and environmentally friendly way to make a known substance from readily available starting materials. Computational chemistry software lets chemists explore possible synthetic pathways before they embark on the time-consuming quest to make a substance. 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 computational methods and theoretical models for predicting chemical reaction pathways, describing chemical phenomena, and designing molecules with desirable properties. At the most fundamental level, I find new computational approaches for describing how electrons bind atoms into molecules. Conventional approaches to this problem are based on the orbital picture. In the orbital picture, electrons are assumed to move quasi-independently. Conventional approaches therefore fail to describe technologically important substances, like unconventional superconductors and spintronic materials, in which the electrons are strongly correlated. My research group is developing alternatives to the orbital picture. First, we are developing alternative computational methods for computing molecular properties; these methods can help chemists decide whether a proposed reaction pathway is plausible. Second, we are developing orbital-free tools for chemical insight. These tools provide intuitive rules that chemists can use to understand and explore new chemical phenomena. In addition, these tools can be used by machine-learning methods, where a computer (instead of a human chemist) gathers 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.