Colloquium: Machine learning in nuclear physics
A Boehnlein, M Diefenthaler, N Sato, M Schram… - Reviews of modern …, 2022 - APS
Advances in machine learning methods provide tools that have broad applicability in
scientific research. These techniques are being applied across the diversity of nuclear …
scientific research. These techniques are being applied across the diversity of nuclear …
Ab initio quantum chemistry with neural-network wavefunctions
Deep learning methods outperform human capabilities in pattern recognition and data
processing problems and now have an increasingly important role in scientific discovery. A …
processing problems and now have an increasingly important role in scientific discovery. A …
Machine learning in nuclear physics at low and intermediate energies
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 …
various research fields due to its exciting and powerful capability of modeling tools used for …
A computational framework for neural network-based variational Monte Carlo with Forward Laplacian
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 …
cutting-edge technique of ab initio quantum chemistry. However, the high computational cost …
Modern applications of machine learning in quantum sciences
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 …
advances in the application of machine learning methods in quantum sciences. We cover …
Message-passing neural quantum states for the homogeneous electron gas
We introduce a message-passing neural-network (NN)-based wave function Ansatz to
simulate extended, strongly interacting fermions in continuous space. Symmetry constraints …
simulate extended, strongly interacting fermions in continuous space. Symmetry constraints …
Neural-network quantum states for periodic systems in continuous space
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 …
systems in the presence of spatial periodicity. Our variational state is parametrized in terms …
Neural-network quantum states for ultra-cold Fermi gases
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 …
transition from a fermionic superfluid Bardeen-Cooper-Schrieffer (BCS) state to a bosonic …
Novel approaches in hadron spectroscopy
M Albaladejo, Ł Bibrzycki, SM Dawid… - Progress in Particle and …, 2022 - Elsevier
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 …
hadrons. The future holds more surprises for us, thanks to new-generation experiments …
[HTML][HTML] Multi-task learning on nuclear masses and separation energies with the kernel ridge regression
XH Wu, YY Lu, PW Zhao - Physics Letters B, 2022 - Elsevier
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 …
masses and separation energies is developed by introducing gradient kernel functions to …