Development of Fast and Accurate Computational Chemistry Methods based on Atom-Centred Potentials and their Application to Crystal Structure Prediction

基于原子中心势的快速准确计算化学方法的发展及其在晶体结构预测中的应用

• 批准号: RGPIN-2021-03080
• 负责人: DiLabio, Gino
• 金额: $ 2.11万
• 依托单位: University of British Columbia
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=742369
• 关键词: Development; Fast; Accurate; Computational; Chemistry

Despite enormous increases in the power of modern computing infrastructure and the development of new quantum mechanical-based methods, there remains a need to develop simulation techniques that enable the prediction of a wide range of chemical properties of large systems with high accuracy. World-leading groups developing quantum mechanics (QM) based methods have focused primarily on creating: 1) semi-empirical methods (SEMs) aimed at structure prediction in systems containing >10,000 atoms (e.g. proteins),1 and 2) improved density-functional theory (DFT) based methods that can be used to predict a wide range of properties for molecules containing up to 100 atoms with reasonable accuracy. My research program aims to develop easy-to-use quantum-based methods with computational costs that are intermediate to SEMs and DFTs, and are applicable to molecules of up to 1000 atoms. We propose to develop methods with target accuracies that are much higher than conventional DFT methods. Therefore, the innovations that emerge from my program will bring valuable new simulation tools to a broad community of users in chemistry, physics, pharmaceutical sciences, and other areas.

尽管现代计算基础架构的力量大大提高以及新的基于量子机械的方法的开发，但仍需要开发模拟技术，从而可以预测具有高精度的大型系统的广泛化学性能。开发量子力学（QM）方法的世界领先的群体主要集中于创建：1）针对包含> 10,000个原子（例如蛋白质）的系统中结构预测的半经验方法（SEMS），1和2）可以改善基于富含分类的物质的基于合理范围的密度官能理论（DFT），这些方法可用于预测富含分类的范围。 我的研究计划旨在开发易于使用的基于量子的方法，其计算成本中间为SEMS和DFT，并且适用于最多1000个原子的分子。我们建议开发具有比常规DFT方法高得多的目标精度的方法。因此，从我的计划中产生的创新将为化学，物理，药物科学和其他领域的广泛用户带来宝贵的新仿真工具。