Improving accuracy and applicability of density functional theory

提高密度泛函理论的准确性和适用性

基本信息

批准号：
1856165
负责人：
Kieron Burke
金额：
$ 50万
依托单位：
University of California-Irvine
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2019
资助国家：
美国
起止时间：
2019-06-01 至 2022-12-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1856165&HistoricalAwards=false
关键词：
Improving accuracy applicability density functional

项目摘要

Professor Kieron J. Burke of the University of California, Irvine is supported by an award from the Chemical Theory, Models and Computational Methods program in the Division of Chemistry to improve the accuracy and applicability of density functional calculations of electronic structure. Density functional theory (DFT) is a popular approach to computationally studying molecules and materials, allowing the design of new pharmaceuticals and materials using computers. The method has been used in more than 30,000 scientific papers each year and has resulted some impressive successes in predicting useful molecules. For example, it has been used to find a better catalyst for making ammonia, an important starting material in chemical processes producing plastics, textiles, fertilizers, dyes and other chemicals. DFT was also used to find the world's hottest superconductor, hydrogen sulfide under pressure. DFT is also used in many machine-learning applications in the physical sciences. However, even the best approximate density functionals, the basic component of DFT, have limited accuracy and ranges of applicability. Dr. Burke and his group are developing DFT methods that use machine learning to create new approximations in order to improve the computational results. He is also studying the origins of the approximations that underly DFT. The resulting improvements in DFT may have significant technological and economic impacts. Dr. Burke broadens understanding of DFT by hosting schools around the world and by training both undergraduate and graduate students in this important cross-disciplinary area of research.Dr. Burke plans to improve DFT calculations in three distinct ways: One approach that is very novel (machine-learning), one very pragmatic and simple (density-corrected DFT), and one very old and deep (semiclassical approximations to functionals). Dr. Burke has pioneered the creation of new functionals with machine learning (ML). These functionals depend on the density everywhere in space, i.e., are radically different for the local and semilocal approximations that form the heart of most modern DFT exchange-correlation functionals. Dr. Burke is investigating approaches to broadening the applicability of ML density functionals. Regarding density-corrected DFT, he is evaluating functionals on Hartree-Fock densities. He uses a new method for the so-called semiclassical approximations which may lead to much more accurate kinetic energy and exchange energy functionals for realistic systems than any in use today.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

加州大学尔湾分校的Kieron J. Burke教授得到了化学理论，模型和计算方法计划的奖项，以提高电子结构的密度功能计算的准确性和适用性。密度功能理论（DFT）是一种流行的计算研究分子和材料的方法，可以使用计算机设计新的药物和材料。该方法每年已在30,000多篇科学论文中使用，并在预测有用的分子方面取得了令人印象深刻的成功。例如，它已被用来找到更好的催化剂来制造氨，这是产生塑料，纺织品，肥料，染料和其他化学物质的化学过程中重要的起始材料。 DFT还用于在压力下找到世界上最热的超导体硫化氢。 DFT也用于物理科学中的许多机器学习应用中。但是，即使是最佳的近似密度功能，即DFT的基本组成部分，也具有有限的适用性和范围。 Burke博士和他的小组正在开发DFT方法，这些方法使用机器学习来创建新的近似值，以改善计算结果。他还在研究DFT基础的近似值的起源。 DFT的最终改善可能会产生重大的技术和经济影响。伯克博士通过在这个重要的跨学科研究领域中培训本科生和研究生来扩大对DFT的了解。伯克计划以三种不同的方式改善DFT计算：一种非常新颖的方法（机器学习），一种非常务实且简单的（密度校正的DFT），以及一种非常古老的和深的（对功能的半经典近似）。伯克博士通过机器学习（ML）开创了新功能的创建。这些功能取决于空间中各地的密度，即，对于构成大多数现代DFT交换相关功能的心脏的局部和半近似值而言，它们在根本上是根本不同的。 Burke博士正在研究扩大ML密度功能的适用性的方法。关于密度校正的DFT，他正在评估Hartree-Fock密度的功能。他对所谓的半经典近似使用了一种新方法，该方法可能会导致比今天使用的任何方法更准确地进行动力和交换能量功能。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛影响的审查标准来通过评估来获得支持的。