Data di Pubblicazione:
2017
Citazione:
Extracting Crystal Chemistry from Amorphous Carbon Structures / V.L. Deringer, G. Csányi, D.M. Proserpio. - In: CHEMPHYSCHEM. - ISSN 1439-4235. - 18:8(2017 Mar 08), pp. 873-877. [10.1002/cphc.201700151]
Abstract:
Carbon allotropes have been explored intensively by abinitio crystal structure prediction, but such methods are limited by the large computational cost of the underlying density functional theory (DFT). Here we show that a novel class of machine-learning-based interatomic potentials can be used for random structure searching and readily predicts several hitherto unknown carbon allotropes. Remarkably, our model draws structural information from liquid and amorphous carbon exclusively, and so does not have any prior knowledge of crystalline phases: it therefore demonstrates true transferability, which is a crucial prerequisite for applications in chemistry. The method is orders of magnitude faster than DFT and can, in principle, be coupled with any algorithm for structure prediction. Machine-learning models therefore seem promising to enable large-scale structure searches in the future.
Tipologia IRIS:
01 - Articolo su periodico
Keywords:
Ab initio calculations; Carbon allotropes; High-throughput screening; Machine learning; Solid-state structures; Atomic and Molecular Physics, and Optics; Physical and Theoretical Chemistry
Elenco autori:
V.L. Deringer, G. Csányi, D.M. Proserpio
Link alla scheda completa: