@inbook{95be9d53ae7849f582f167b1c35e87b4,
title = "Development of Exchange-Correlation Functionals Assisted by Machine Learning",
abstract = "With the recent rapid progress in the machine-learning (ML), there have emerged a new approach using the ML methods for developing the exchange-correlation functionals of density functional theory. In this chapter, we review how the ML tools are used for this and the performances achieved recently. It is revealed that the ML, not being opposed to the analytical methods, complements human intuition and advances the development of the first-principles calculation with desired accuracy.",
keywords = "Density functional theory, Electronic structure, Machine learning, Neural network",
author = "Ryo Nagai and Ryosuke Akashi",
note = "Publisher Copyright: {\textcopyright} 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.",
year = "2023",
doi = "10.1007/978-3-031-37196-7\_4",
language = "English",
series = "Challenges and Advances in Computational Chemistry and Physics",
publisher = "Springer Science and Business Media B.V.",
pages = "91--112",
booktitle = "Challenges and Advances in Computational Chemistry and Physics",
}