Machine learning molecular dynamics for the simulation of infrared spectra† †Electronic supplementary information (ESI) available. See DOI: 10.1039/c7sc02267k

نویسندگان

  • Michael Gastegger
  • Jörg Behler
  • Philipp Marquetand
چکیده

1 Electronic Structure Calculations All electronic structure calculations were carried out with the ORCA program1. Density functional theory calculations on the BP862–6 (methanol and the tripeptide) and BLYP2–4,7 (only tripeptide) level of theory were performed using the def2-SVP basis8 set and the RI approximation with the def2-SVP/J auxilary basis set.9,10 B2PLYP11 computations (n-alkanes) used the def2-TZVPP basis8 set and were accelerated using the RI-MP2 algorithm10 in combination with the def2-TZVPP/J12 and def2-TZVPP/C13 auxiliary basis sets. In both cases, SCF convergence criteria were set to tight. For B2PLYP, an integration grid of size 4 was used.

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Machine learning molecular dynamics for the simulation of infrared spectra.

Machine learning has emerged as an invaluable tool in many research areas. In the present work, we harness this power to predict highly accurate molecular infrared spectra with unprecedented computational efficiency. To account for vibrational anharmonic and dynamical effects - typically neglected by conventional quantum chemistry approaches - we base our machine learning strategy on ab initio ...

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عنوان ژورنال:

دوره 8  شماره 

صفحات  -

تاریخ انتشار 2017