Electronic structure theory: Merging wave function theory, density functional theory, and machine learning.

电子结构理论：融合波函数理论、密度泛函理论和机器学习。

• 批准号: RGPIN-2022-04971
• 负责人: Ernzerhof, Matthias
• 金额: $ 2.62万
• 依托单位: Université de Montréal
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=748672
• 关键词: Electronic; structure; theory; Merging; wave

Electronic structure theory provides the basis for our understanding of much of chemistry, biochemistry, material science, condensed matter physics, etc. It also provides the tools for computational modelling in the various areas mentioned. The proposed research aims at advancing electronic structure theory. In particular, we focus on density functional theory (DFT), where we address the exchange-correlation energy among electrons. This is the crucial component of Kohn-Sham DFT and it accounts for the complicated interactions between electrons. Recently, there has been enormous progress in this area which made Kohn-Sham DFT one of the most used tools in chemistry and the most cited topic in the history of the physical sciences. However, there remain numerous systems and properties for which the existing approximations and computational modelling tools are not reliable. Building on and continuing our previous work, our research program aims at deepening the understanding of the exchange-correlation energy and at improving upon existing exchange-correlation functionals. In the last decade, artificial intelligence and machine learning emerged as powerful tools that can be of use in virtually all areas of science and technology. The design of approximations and improved computational modelling tools in electronic structure theory is very challenging and we develop and adapt machine learning algorithms to perform an increasing share of the work. With machine learning, we can solve problems in the construction of approximations that are insurmountable for humans. Combining our experience in electronic structure theory with rapidly evolving artificial intelligence, we will provide improved computational tools, further simplifying the design of new materials and compounds. Furthermore, we intend to continue our work in the area of molecular electronics where molecules are connected to contacts to serve as electronic components and the current that passes through the molecules is measured as a function of the applied voltage. In the past, we developed simple models for molecular electronic devices (MEDs) that enabled us to predict new phenomena, some of which have been verified experimentally. In the proposed research we want to improve and extend our theories, in particular, we propose to develop theories that can describe the impact of electron-electron interaction on MEDs. While electronic structure theory has made enormous progress, most of its tools are not suitable for molecular conductors and with our work, we want to improve the description of exchange and correlation effects among electrons in MEDs.

电子结构理论为我们理解化学，生物化学，材料科学，凝聚态物理学等提供了基础，它也为上述各个领域的计算建模提供了工具。该研究旨在推进电子结构理论。特别是，我们专注于密度泛函理论（DFT），在那里我们解决电子之间的交换相关能。这是Kohn-Sham DFT的关键组成部分，它解释了电子之间复杂的相互作用。最近，在这一领域取得了巨大的进展，这使得Kohn-Sham DFT成为化学中最常用的工具之一，也是物理科学史上引用最多的主题。然而，仍然有许多系统和属性，现有的近似和计算建模工具是不可靠的。我们的研究计划以我们之前的工作为基础并继续进行，旨在加深对交换相关能量的理解并改进现有的交换相关泛函。在过去的十年中，人工智能和机器学习成为强大的工具，几乎可以在所有科学和技术领域使用。在电子结构理论中设计近似和改进的计算建模工具是非常具有挑战性的，我们开发和调整机器学习算法来执行越来越多的工作。通过机器学习，我们可以解决人类无法克服的近似构建问题。结合我们在电子结构理论方面的经验和快速发展的人工智能，我们将提供改进的计算工具，进一步简化新材料和化合物的设计。此外，我们打算继续我们在分子电子学领域的工作，其中分子连接到触点以用作电子元件，并且通过分子的电流被测量为施加电压的函数。在过去，我们为分子电子器件（MED）开发了简单的模型，使我们能够预测新的现象，其中一些已经被实验验证。在拟议的研究中，我们希望改进和扩展我们的理论，特别是，我们建议发展理论，可以描述电子-电子相互作用对MED的影响。虽然电子结构理论已经取得了巨大的进步，但它的大多数工具都不适合分子导体，并且通过我们的工作，我们希望改进MED中电子之间的交换和关联效应的描述。