Machine learning molecular dynamics for the simulation of infrared spectra

By Michael Gastegger, J. Behler, Philipp Marquetand. 2017

389 citations recorded by the source corpus.

Artificial neural networks are combined with molecular dynamics to simulate molecular infrared spectra including anharmonicities and temperature effects.

Subfields: Atomic and Molecular Physics, and Optics, Materials Chemistry

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Atomic and Molecular Physics, and Optics

In Materials Chemistry

Citations in the corpus

Inverse design of 3d molecular structures with conditional generative neural networks (2021, background): “While the small organic compounds considered in this work are well represented by QM9, the model might benefit from enhancing the training data using representative building blocks such as ”amons” [64] or other fragmentation methods [65, 66].”
Machine-learned potentials for next-generation matter simulations (2021, methodology): “Point charges and multi-pole moments can furthermore be used to computationally predict infrared spectra and weak intermolecular interaction energies in organic crystals 61–63 .”
Outsmarting Quantum Chemistry Through Transfer Learning (2018, background): “Recent chemistry related work reports on ML models for chemical reactions 21,22 , potential energy surfaces 23–27 , forces 28–30 , atomization energies 31,32 , atomic partial charges 32– 35 , molecular dipoles 26,36,37 , materials discovery 38–40 , and protein-ligand complex scoring 41 .”
Machine learning for molecular simulation (2019, background): “Alternatively, sophisticated ML approaches that can accurately reproduce the global potential energy surface (PES) for elemental materials [7, 52, 9, 53, 14, 54] and small molecules [55, 56, 11, 10, 57, 54, 58] have been recently developed (see Fig.”