TY - JOUR
T1 - Comparing the Expense and Accuracy of Methods to Simulate Atomic Vibrations in Rubrene
AU - Dettmann, Makena A.
AU - Cavalcante, Lucas S.R.
AU - Magdaleno, Corina
AU - Masalkovaitė, Karina
AU - Vong, Daniel
AU - Dull, Jordan T.
AU - Rand, Barry P.
AU - Daemen, Luke L.
AU - Goldman, Nir
AU - Faller, Roland
AU - Moulé, Adam J.
N1 - Funding Information:
This research was supported by the Department of Energy, Basic Energy Sciences, Award DE-SC0010419, including salary for M.A.D., L.S.R.C., D.V., and A.J.M. J.T.D. and B.P.R. acknowledge support from the National Science Foundation under award no. ECCS-1709222. R.F. acknowledges support from the National Science Foundation under award no. CBET-1911267. K.M. was sponsored by the Wasson Honors Program of the Chemical Engineering Department at the University of California, Davis. This research used resources of the National Energy Research Scientific Computing Center (NERSC), a DOE Office of Science User Facility supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231. The INS spectrum was measured at the Spallation Neutron Source, a DOE Office of Science User Facility operated by the Oak Ridge National Laboratory, partly supported by LLNL under Contract DE-AC52-07NA27344.
Publisher Copyright:
© 2021 American Chemical Society.
PY - 2021/12/14
Y1 - 2021/12/14
N2 - 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.
AB - 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.
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U2 - 10.1021/acs.jctc.1c00747
DO - 10.1021/acs.jctc.1c00747
M3 - Article
C2 - 34818006
AN - SCOPUS:85120536022
SN - 1549-9618
VL - 17
SP - 7313
EP - 7320
JO - Journal of Chemical Theory and Computation
JF - Journal of Chemical Theory and Computation
IS - 12
ER -