Advances in machine learning methods provide tools that have broad applicability in
scientific research. These techniques are being applied across the diversity of nuclear …

Deep learning methods outperform human capabilities in pattern recognition and data
processing problems and now have an increasingly important role in scientific discovery. A …

Abstract Machine learning (ML) is becoming a new paradigm for scientific research in
various research fields due to its exciting and powerful capability of modeling tools used for …

Neural network-based variational Monte Carlo (NN-VMC) has emerged as a promising
cutting-edge technique of ab initio quantum chemistry. However, the high computational cost …

In these Lecture Notes, we provide a comprehensive introduction to the most recent
advances in the application of machine learning methods in quantum sciences. We cover …

We introduce a message-passing neural-network (NN)-based wave function Ansatz to
simulate extended, strongly interacting fermions in continuous space. Symmetry constraints …

We introduce a family of neural quantum states for the simulation of strongly interacting
systems in the presence of spatial periodicity. Our variational state is parametrized in terms …

Ultra-cold Fermi gases exhibit a rich array of quantum mechanical properties, including the
transition from a fermionic superfluid Bardeen-Cooper-Schrieffer (BCS) state to a bosonic …

The last two decades have witnessed the discovery of a myriad of new and unexpected
hadrons. The future holds more surprises for us, thanks to new-generation experiments …

A multi-task learning (MTL) framework, called gradient kernel ridge regression, for nuclear
masses and separation energies is developed by introducing gradient kernel functions to …