Improving accuracy and applicability of density functional theory

加州大学欧文分校的基隆·J·伯克教授得到了化学部化学理论、模型和计算方法项目的支持，以提高电子结构密度泛函计算的准确性和适用性。密度泛函理论(DFT)是一种流行的计算研究分子和材料的方法，允许使用计算机设计新的药物和材料。这种方法每年被用于3万多篇科学论文，并在预测有用分子方面取得了一些令人印象深刻的成功。例如，它被用来寻找一种更好的催化剂来制造氨，氨是生产塑料、纺织品、化肥、染料和其他化学品的化学过程中的重要起始原料。DFT还被用来发现世界上最热的超导体，压力下的硫化氢。离散傅立叶变换也被用于物理科学中的许多机器学习应用。然而，作为密度泛函的基本组成部分，即使是最佳近似密度泛函，其精度和适用范围也是有限的。伯克博士和他的团队正在开发DFT方法，这种方法使用机器学习来创建新的近似，以改善计算结果。他还在研究低于DFT的近似的起源。随之而来的DFT改进可能会产生重大的技术和经济影响。伯克博士通过在世界各地举办学校，并在这一重要的跨学科研究领域培训本科生和研究生，拓宽了对DFT的理解。Burke计划在三个不同的方面改进DFT计算：一种是非常新颖的方法(机器学习)，一种是非常实用和简单的(密度校正的DFT)，以及一种非常古老和深入的方法(泛函的半经典近似)。Burke博士开创了使用机器学习(ML)创建新函数的先河。这些泛函依赖于空间中的密度，也就是说，对于构成大多数现代DFT交换相关泛函核心的局域和半局域近似，这些泛函是完全不同的。伯克博士正在研究扩大ML密度泛函适用性的方法。关于密度校正DFT，他正在计算关于Hartree-Fock密度的泛函。他使用了一种新的方法来进行所谓的半经典近似，这可能会导致现实系统的动能和交换能量泛函比今天使用的任何泛函都要准确得多。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.