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

尽管现代计算基础设施的能力和基于量子力学的新方法的发展有了巨大的增长，但仍然需要开发模拟技术，以便能够高精度地预测大型系统的广泛化学性质。世界领先的团队开发基于量子力学（QM）的方法主要集中在创建：1)半经验方法（sem），旨在预测包含bbb10万个原子（例如蛋白质）的系统的结构；1和2)改进的密度泛函数理论（DFT）为基础的方法，可用于预测包含多达100个原子的分子的广泛性质，具有合理的精度。我的研究计划旨在开发易于使用的基于量子的方法，其计算成本介于sem和dft之间，并且适用于多达1000个原子的分子。我们建议开发目标精度远高于传统DFT方法的方法。因此，从我的程序中出现的创新将为化学，物理，制药科学和其他领域的广大用户带来有价值的新模拟工具。

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.