EAGER: Density functionals from Machine Learning

加州大学欧文分校的Kieron Burke获得了化学学部化学理论、模型和计算方法项目的Eager奖。该奖项由材料研究部的凝聚态物质和材料理论项目以及数学科学部的计算和数据支持科学项目共同资助。密度泛函理论（DFT）是现代电子结构计算的核心，在化学和材料设计中发挥着越来越重要的作用。今天的近似是由灵感和实用主义的邪恶联盟创造的。他们的进步是缓慢和不系统的。Burke和他的同事们正在将机器学习（ML）应用于近似密度泛函的问题。ML提供了一种全新的近似函数的方法，最大限度地利用了DFT形式化。他们已经证明，在一个简单的模型情况下，可以达到自洽密度的化学精度，并且可以给出近似可靠性的度量。EAGER拨款的目标是通过一系列明确定义的小步骤来解决问题，将这些方法应用于DFT，克服任何挑战，并尽可能快地达到电子结构计算的现实世界。该项目创建了一个全新的理论/计算子领域，其中机器学习（ML），计算机科学的一个分支，被应用于电子结构问题，理论物理和化学的一个分支，允许通过解决量子力学方程来预测新的分子和材料。新的算法，在机器学习研究的前沿，正在开发，以应用机器学习来寻找密度泛函，需要解决电子结构。这是物理科学和计算机科学真正的协同作用。这一提议的成功将彻底改变材料设计，使数以百万计的原子可以被处理，而不是目前方法中的数百个原子。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中的裂纹扩展。

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.