Machine learning molecular dynamics for the simulation of infrared spectra.

ABSTRACT: 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 molecular dynamics simulations. While these simulations are usually extremely time consuming even for small molecules, we overcome these limitations by leveraging the power of a variety of machine learning techniques, not only accelerating simulations by several orders of magnitude, but also greatly extending the size of systems that can be treated. To this end, we develop a molecular dipole m

SUBMITTER: Gastegger M 

PROVIDER: S-EPMC5636952 | biostudies-literature | 2017 Oct

REPOSITORIES: biostudies-literature