Comparing the Expense and Accuracy of Methods to Simulate Atomic Vibrations in Rubrene

Makena A. Dettmann, Lucas S.R. Cavalcante, Corina Magdaleno, Karina Masalkovaitė, Daniel Vong, Jordan T. Dull, Barry P. Rand, Luke L. Daemen, Nir Goldman, Roland Faller, Adam J. Moulé

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

Atomic vibrations can inform about materials properties from hole transport in organic semiconductors to correlated disorder in metal-organic frameworks. Currently, there are several methods for predicting these vibrations using simulations, but the accuracy-efficiency tradeoffs have not been examined in depth. In this study, rubrene is used as a model system to predict atomic vibrational properties using six different simulation methods: density functional theory, density functional tight binding, density functional tight binding with a Chebyshev polynomial-based correction, a trained machine learning model, a pretrained machine learning model called ANI-1, and a classical forcefield model. The accuracy of each method is evaluated by comparison to the experimental inelastic neutron scattering spectrum. All methods discussed here show some accuracy across a wide energy region, though the Chebyshev-corrected tight-binding method showed the optimal combination of high accuracy with low expense. We then offer broad simulation guidelines to yield efficient, accurate results for inelastic neutron scattering spectrum prediction.

Original languageEnglish (US)
Pages (from-to)7313-7320
Number of pages8
JournalJournal of Chemical Theory and Computation
Volume17
Issue number12
DOIs
StatePublished - Dec 14 2021

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Physical and Theoretical Chemistry

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