Machine learning corrected quantum dynamics calculations

A. Jasinski, J. Montaner, R. C. Forrey, B. H. Yang, P. C. Stancil, N. Balakrishnan, J. Dai, R. A. Vargas-Hernández, R. V. Krems

Research output: Contribution to journalArticlepeer-review

12 Scopus citations


Quantum scattering calculations for all but low-dimensional systems at low energies must rely on approximations. All approximations introduce errors. The impact of these errors is often difficult to assess because they depend on the Hamiltonian parameters and the particular observable under study. Here, we illustrate a general, system- A nd approximation-independent, approach to improve the accuracy of quantum dynamics approximations. The method is based on a Bayesian machine learning (BML) algorithm that is trained by a small number of exact results and a large number of approximate calculations, resulting in ML models that can generalize exact quantum results to different dynamical processes. Thus, a ML model trained by a combination of approximate and rigorous results for a certain inelastic transition can make accurate predictions for different transitions without rigorous calculations. This opens the possibility of improving the accuracy of approximate calculations for quantum transitions that are out of reach of exact scattering theory.

Original languageEnglish (US)
Article number032051
JournalPhysical Review Research
Issue number3
StatePublished - Aug 2020

All Science Journal Classification (ASJC) codes

  • General Physics and Astronomy


Dive into the research topics of 'Machine learning corrected quantum dynamics calculations'. Together they form a unique fingerprint.

Cite this