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Aiming for Chemical Accuracy in Ground-state Density Functional Theory

追求基态密度泛函理论的化学准确性

基本信息

批准号：
2154371
负责人：
Kieron Burke
金额：
$ 51万
依托单位：
University of California-Irvine
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2022
资助国家：
美国
起止时间：
2022-05-01 至 2025-04-30
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2154371&HistoricalAwards=false
关键词：
Aiming Chemical Accuracy Ground state

项目摘要

With support from the Chemical Theory, Models and Computational Methods program in the Division of Chemistry, Professor Kieron Burke of the University of California, Irvine will endeavor to improve ground state density functional theory (DFT). Each year, more than 50,000 scientific papers use DFT to predict the properties of molecules and solids, and to design new pharmaceutical drugs and materials. About 1/3 of US supercomputer use is devoted to this task. But such calculations are limited due to poor performance of approximations. The aim of this work is to develop a new mathematical framework that has the promise of breakthroughs in both accuracy and speed of DFT calculations. Improvements in speed or accuracy of DFT calculations on the scale of this proposal would transform both the nature and capabilities of most DFT applications. Contrbuting to education and outreach, graduate and undergraduate students involved in this project will be trained to acquire high level knowledge of DFT, in a diverse and multi-national research group. A self-guided online course in Machine Learning in the Physical Sciences, developed by the Burke group and already available to all UC-Irvine students and faculty, will be made available to the public.This proposal combines semiclassical methods (expansions in powers of h, Planck's constant), density-corrected DFT for separating energy errors from density-driven errors, and machine learning for finding approximate functionals from data. The aim is to go from proof-of-principle to approximate density functionals that can be applied to routine calculations. If successful, the research being undertaken under this award has the potential (a) to transform the basic starting point (generalized gradient approximations) of all DFT calculations into a new form; (b) to unite the functionals used in materials with those in chemistry; (c) to produce chemical accuracy for strong bonds at equilibrium, and (d) to apply to the non-interacting kinetic energy, and so impact orbital-free DFT. In short, success here would likely contribute to all fields currently using DFT calculations, and thus, the potential to achieve far-reaching broader scientific impact.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.

在化学系化学理论、模型和计算方法项目的支持下，加州大学欧文分校的Kieron Burke教授将奋进改进基态密度泛函理论（DFT）。每年有超过50，000篇科学论文使用DFT来预测分子和固体的性质，并设计新的药物和材料。美国大约1/3的超级计算机用于这一任务。但由于近似性能差，这样的计算是有限的。这项工作的目的是开发一个新的数学框架，有希望突破DFT计算的精度和速度。在这个提议的规模上，DFT计算的速度或准确性的改进将改变大多数DFT应用的性质和能力。除了教育和推广外，参与该项目的研究生和本科生将在一个多元化的多国研究小组中接受培训，以获得DFT的高水平知识。由Burke小组开发的一个物理科学中的机器学习在线自助课程将向公众开放，该课程已经提供给所有加州大学欧文分校的学生和教师。该课程结合了半经典方法（普朗克常数h的幂次展开），用于分离能量误差和密度驱动误差的密度泛函校正DFT，以及用于从数据中找到近似泛函的机器学习。目的是从原理证明到近似密度泛函，可以应用于常规计算。如果成功，在该奖项下进行的研究有可能（a）改变基本出发点（B）将材料中的泛函与化学中的泛函统一起来;（c）在平衡时产生强键的化学精确度，以及（d）应用于非相互作用动能，从而影响无轨道DFT。简而言之，这一奖项的成功将有助于目前使用DFT计算的所有领域，从而有可能实现深远的更广泛的科学影响。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。