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Machine-Learning for Static and Dynamic Electronic Structure Theory

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Machine Learning in Molecular Sciences

Part of the book series: Challenges and Advances in Computational Chemistry and Physics ((COCH,volume 36))

Abstract

Machine learning has emerged as a powerful technique for processing large and complex datasets. Recently it has been utilized for both improving the accuracy and accelerating the computational speed of electronic structure theory. In this chapter, we provide the theoretical background of both density functional theory, the most widely used electronic structure method, and machine learning on a generally accessible level. We provide a brief overview of the most impactful results in recent times. We, further, showcase how machine learning is used to advance static and dynamic electronic structure calculations with concrete examples. This chapter highlights that fusing concepts of machine learning and density functional theory holds the promise to greatly advance electronic structure calculations enabling unprecedented applications for in-silico materials discovery and the search for novel chemical reaction pathways.

Part of this chapter has been reproduced from Ref. [1] under a CC BY 4.0 license, https://creativecommons.org/licenses/by/4.0/.

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    Lenz Fiedler, Karan Shah & Attila Cangi

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Fiedler, L., Shah, K., Cangi, A. (2023). Machine-Learning for Static and Dynamic Electronic Structure Theory. In: Qu, C., Liu, H. (eds) Machine Learning in Molecular Sciences. Challenges and Advances in Computational Chemistry and Physics, vol 36. Springer, Cham. https://doi.org/10.1007/978-3-031-37196-7_5

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