A novel Bayesian reconstruction of the configurational density of states

Felipe Moreno, Sergio Davis, Joaquín Peralta, Simón Poblete

Research output: Contribution to journalArticlepeer-review


In this work, we present the development and implementation of a novel Bayesian method for the reconstruction of the density of states (DOS) of a system using energy data obtained from Monte Carlo simulations. This method uses a trial family of functions with adjustable parameters, which are optimized using the Bayes theorem. The measurements can be done in any ensemble with a known distribution function, which significantly helps to overcome energy traps and explore the conformation space thoroughly. We apply our algorithm on a test Potts model system and find that our implementation can find the correct DOS in a reasonable amount of time. Moreover, if the trial function is suitable enough, the DOS found by the algorithm is very close to the actual DOS.

Original languageEnglish
Article number112326
JournalComputational Materials Science
Publication statusPublished - Sept 2023


  • Algorithm
  • Bayes theorem
  • Density of states

ASJC Scopus subject areas

  • General Computer Science
  • General Chemistry
  • General Materials Science
  • Mechanics of Materials
  • General Physics and Astronomy
  • Computational Mathematics


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