EAGER: Density functionals from Machine Learning

EAGER：机器学习中的密度泛函

• 批准号: 1240252
• 负责人: Kieron Burke
• 金额: $ 30万
• 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: 2012
• 资助国家: 美国
• 起止时间: 2012-05-01 至 2015-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1240252&HistoricalAwards=false
• 关键词: EAGER; Density; functionals; Machine; Learning

Kieron Burke of the University of California, Irvine, is supported by an Eager award from the Chemical Theory, Models and Computational Methods program in the Chemistry Division. The award is cofunded by the Condensed Matter and Materials Theory program in the Division of Materials Research and the Computational and Data Enabled Science Program in the Division of Mathematical Science. Density functional theory (DFT) is at the heart of modern electronic structure calculations, which play an ever increasing role in chemical and material design. Present-day approximations are created by an unholy alliance of inspiration and pragmatism. Progress in their improvement is slow and unsystematic. Burke and his colleagues are applying Machine Learning (ML) to the problem of approximating density functionals. ML provides a totally new way to approximate functionals that takes maximum advantage of DFT formalism. They have demonstrated that chemical accuracy on self-consistent densities can be reached for a simple model case, and a measure of the reliability of the approximation can be given. The goal of the EAGER grant is to develop these methods in application to DFT, overcoming any challenges, and reaching the real world of electronic structure calculations as quickly as possible, by approaching the problem in a well-defined series of small steps.This project creates an entirely new subfield of theory/computation, in which machine learning (ML), a branch of computer science, is applied to electronic structure problems, a branch of theoretical physics and chemistry that allows prediction of new molecules and materials by solving the equations of quantum mechanics. New algorithms, at the cutting edge of ML research, are being developed in order to apply ML to find density functionals, needed for solving electronic structure. This is a true synergy of physical and computer sciences. Success of this proposal would revolutionize materials design, allowing millions of atoms to be treated instead of hundreds by present methods. This would truly transform predictive capability in a broad range of scientific and technological problems, from biomolecular liquid simulations to crack propagation in materials.

加利福尼亚大学尔湾分校的基隆·伯克（Kieron Burke）得到了化学理论，模型和计算方法计划的渴望奖。 该奖项由材料研究部以及数学科学部的计算和数据启用的计算和数据的科学计划中的凝结物质和材料理论计划进行了奖励。 密度功能理论（DFT）是现代电子结构计算的核心，在化学和材料设计中起着越来越多的作用。当今的近似是由灵感和实用主义的邪恶联盟创建的。他们的改进进展缓慢且非系统性。 伯克和他的同事正在将机器学习（ML）应用于近似密度功能的问题。 ML为近似功能提供了一种全新的方法，该功能最大程度地利用了DFT形式主义。 他们已经证明，对于简单的模型情况，可以达到自洽密度的化学精度，并且可以给出近似值的可靠性。渴望赠款的目的是通过在一系列明确的小步骤中解决问题来制定这些方法，克服任何挑战，克服任何挑战，并尽快达到电子结构计算的现实世界。通过求解量子力学方程来分子和材料。在ML研究的最前沿，正在开发新的算法，以应用ML以找到密度功能，以解决电子结构。这是物理和计算机科学的真正协同作用。该提案的成功将彻底改变材料设计，从而使数百万原子可以通过当前方法来处理。这将真正改变广泛的科学和技术问题的预测能力，从生物分子液体模拟到材料的裂纹繁殖。