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Machine learning techniques have become standard scientific tools across several fields. Quantum many-body theory is no exception, with a recent explosion of applications in domains that range from spin systems, to quantum chemistry and nuclear physics. This workshop is devoted to discussing machine-learning tools that aim at directly solving the Schrödinger equation in a many-particle context. These tools typically exploit the outstanding variational properties of neural networks, including first- and second-quantized versions. We aim at bringing together quantum many-body practitioners, within and outside the nuclear domain, and physics-focused machine learning experts, to review past achievements and recent advances in solving quantum many-body problems with machine learning tools. By bringing together different communities, we want to take a global look at the state-of-the-art in the field; to identify new avenues and recent meaningful advances; and to stimulate cross-fertilisation among fields to consolidate the use of machine-learning tools in the many-body domain.
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