Accurate computation of quantum excited states with neural networks

We present an algorithm to estimate the excited states of a quantum system by variational Monte Carlo, which has no free parameters and requires no orthogonalization of the states, instead transforming the problem into that of finding the ground state of an expanded system. Arbitrary observables can...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	Science (American Association for the Advancement of Science) 2024-08, Vol.385 (6711), p.eadn0137
Hauptverfasser:	Pfau, David, Axelrod, Simon, Sutterud, Halvard, von Glehn, Ingrid, Spencer, James S
Format:	Artikel
Sprache:	eng
Schlagworte:	Accuracy Adiabatic Artificial neural networks Atomic properties Benchmarks Benzene Chemical bonds Computation Computer applications Condensed matter physics Couplings Deep learning Estimates Excitation First principles Fluorescent indicators Ground state Hamiltonian functions Hydrocarbons Light emitting diodes Machine learning Mathematical analysis Mathematical models Mathematics Molecular modelling Networks Neural networks Nuclear physics Organic Chemistry Oscillator strengths Photovoltaic cells Physics Potential energy Prior Learning Quantum chemistry Quantum dots Solar cells Wave functions
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Schreiben Sie den ersten Kommentar!