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.

在化学理论，模型和计算方法计划的支持下，加利福尼亚大学的基隆·伯克教授将努力改善基态密度功能理论（DFT）。每年，有50,000多篇科学论文使用DFT来预测分子和固体的特性，并设计新的药物和材料。大约1/3的超级计算机使用专门用于此任务。但是，由于近似性能较差，这种计算受到限制。这项工作的目的是开发一个新的数学框架，该框架有望在DFT计算的准确性和速度方面取得突破。 DFT计算的速度或准确性在此提案的规模上的提高将改变大多数DFT应用的性质和功能。涉及该项目的教育和外展，研究生和本科生将接受培训，以在潜水员和跨国研究小组中获得高水平的DFT知识。由Burke组开发的机器学习中的自学在线课程，并已向所有UC Irvine学生和教职员工提供，将向公众提供。该提案结合了半经典方法（HE，Planck的功能扩展，Planck常数的功能），密度适应的DFT，用于分离数据以分离数据，以从密度范围内分离出数据，以使其从密度范围内分开，并逐步研究了机器，并且计算机的计算机和计算机都可以分开。目的是从原理证明转变为可以应用于常规计算的近似密度功能。如果成功的话，根据该奖项进行的研究具有（a）将所有DFT计算的基本起点（广义梯度近似）转变为新形式的潜力；（b）将用于化学的功能统一材料中使用的功能；（c）在平衡时产生强键的化学精度，以及（d）应用于非相互作用的动能，因此撞击了无轨道的DFT。简而言之，这里的成功可能会为当前使用DFT计算做出贡献，因此，实现更广泛的广泛科学影响的潜力。该奖项反映了NSF的法定任务，并通过使用该基金会的知识分子的优点和更广泛的影响来审查标准，被认为是通过评估而被视为珍贵的支持。