A Machine-Learning Surrogate Model for ab initio Electronic Correlations at Extreme Conditions

Tobias Dornheim; Zhandos Moldabekov; Attila Cangi

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arXiv · 2104.02941

A Machine-Learning Surrogate Model for ab initio Electronic Correlations at Extreme Conditions

Tobias Dornheim·Zhandos Moldabekov·Attila Cangi

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Abstract

The electronic structure in matter under extreme conditions is a challenging complex system prevalent in astrophysical objects and highly relevant for technological applications. We show how machine-learning surrogates in terms of neural networks have a profound impact on the efficient modeling of matter under extreme conditions. We demonstrate the utility of a surrogate model that is trained on \emph{ab initio} quantum Monte Carlo data for various applications in the emerging field of warm dense matter research.

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Tobias Dornheim, Zhandos Moldabekov, Attila Cangi. 2021-04-07. A Machine-Learning Surrogate Model for ab initio Electronic Correlations at Extreme Conditions. https://arxiv.org/abs/2104.02941

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