AI reveals unexpected new physics in dusty plasma
Physicists have used a machine-learning method to identify surprising new twists on the non-reciprocal forces governing a many-body system.
Many-body techniques are computational and theoretical methods used to describe and solve quantum or classical systems with a large number of interacting particles, where correlations invalidate simple mean-field or single-particle approximations. They encompass approaches such as Green’s function methods, diagrammatic perturbation theory, coupled-cluster theory, configuration interaction, quantum Monte Carlo, density-matrix renormalization group, and dynamical mean-field theory. These techniques systematically incorporate many-body correlations into observables like excitation spectra, ground-state energies, and response functions, and are central in condensed matter physics, nuclear structure, quantum chemistry, and strongly correlated materials research.
Physicists have used a machine-learning method to identify surprising new twists on the non-reciprocal forces governing a many-body system.
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