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On representing chemical environments
We review some recently published methods to represent atomic neighborhood environments, and analyze their relative merits in terms of their faithfulness and suitability for fitting potential energy
Gaussian approximation potentials: the accuracy of quantum mechanics, without the electrons.
We introduce a class of interatomic potential models that can be automatically generated from data consisting of the energies and forces experienced by atoms, as derived from quantum mechanical
Reinforcement of single-walled carbon nanotube bundles by intertube bridging
Stable links between neighbouring carbon nanotubes within bundles are introduced using moderate electron-beam irradiation inside a transmission electron microscope, showing that interstitial carbon atoms formed during irradiation in addition to carboxyl groups, can independently lead to bridge formation between neighbouring nanot tubes.
Surface diffusion: the low activation energy path for nanotube growth.
The temperature dependence of the growth rate of carbon nanofibers by plasma-enhanced chemical vapor deposition with Ni, Co, and Fe catalysts is presented and a low activation energy of 0.4 eV is extrapolated, much lower than for thermal deposition.
Comparing molecules and solids across structural and alchemical space.
This work discusses how one can combine such local descriptors using a regularized entropy match (REMatch) approach to describe the similarity of both whole molecular and bulk periodic structures, introducing powerful metrics that enable the navigation of alchemical and structural complexities within a unified framework.
Hybrid atomistic simulation methods for materials systems
We review recent progress in the methodology of hybrid quantum/classical (QM/MM) atomistic simulations for solid-state systems, from the earliest reports in 1993 up to the latest results. A unified
Efficient sampling of atomic configurational spaces.
A discretely valued order parameter is identified with basins and suprabasins of the PES, allowing a straightforward and unambiguous definition of macroscopic states of an atomistic system and the evaluation of the associated free energies.
Gaussian approximation potentials: A brief tutorial introduction
© 2015 Wiley Periodicals, Inc. We present a swift walk-through of our recent work that uses machine learning to fit interatomic potentials based on quantum mechanical data. We describe our Gaussian
Machine learning based interatomic potential for amorphous carbon
We introduce a Gaussian approximation potential (GAP) for atomistic simulations of liquid and amorphous elemental carbon. Based on a machine learning representation of the density-functional theory
Edge-functionalized and substitutionally doped graphene nanoribbons: Electronic and spin properties
Graphene nanoribbons are the counterpart of carbon nanotubes in graphene-based nanoelectronics. We investigate the electronic properties of chemically modified ribbons by means of density functional