On representing chemical environments
- A. P. Bart'ok, R. Kondor, Gábor Csányi
- Computer Science
- 14 September 2012
It is demonstrated that certain widely used descriptors that initially look quite different are specific cases of a general approach, in which a finite set of basis functions with increasing angular wave numbers are used to expand the atomic neighborhood density function.
Gaussian approximation potentials: the accuracy of quantum mechanics, without the electrons.
- A. Bartók, M. Payne, R. Kondor, Gábor Csányi
- PhysicsPhysical Review Letters
- 6 October 2009
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…
Machine learning based interatomic potential for amorphous carbon
- Volker L. Deringer, Gábor Csányi
- Materials Science
- 10 November 2016
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…
Surface diffusion: the low activation energy path for nanotube growth.
- S. Hofmann, Gábor Csányi, A. Ferrari, M. Payne, J. Robertson
- PhysicsPhysical Review Letters
- 12 July 2005
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.
Gaussian approximation potentials: A brief tutorial introduction
- A. Bartók, Gábor Csányi
- Computer Science
- 4 February 2015
The Gaussian approximation potentials (GAP) framework is described, a variety of descriptors are discussed, how to train the model on total energies and derivatives, and the simultaneous use of multiple models of different complexity are discussed.
A Performance and Cost Assessment of Machine Learning Interatomic Potentials.
- Yunxing Zuo, Chi Chen, S. Ong
- Materials ScienceJournal of Physical Chemistry A
- 20 June 2019
A comprehensive evaluation of ML-IAPs based on four local environment descriptors --- atom-centered symmetry functions (ACSF), smooth overlap of atomic positions (SOAP), the Spectral Neighbor Analysis Potential (SNAP) bispectrum components, and moment tensors --- using a diverse data set generated using high-throughput density functional theory (DFT) calculations.
Reinforcement of single-walled carbon nanotube bundles by intertube bridging
- A. Kis, Gábor Csányi, L. Forró
- Materials ScienceNature Materials
- 1 March 2004
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.
Edge-functionalized and substitutionally doped graphene nanoribbons: Electronic and spin properties
- F. Cervantes-Sodi, Gábor Csányi, S. Piscanec, A. Ferrari
- PhysicsPhysical review B
- 15 November 2007
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…
Machine Learning a General-Purpose Interatomic Potential for Silicon
- A. Bartók, J. Kermode, N. Bernstein, Gábor Csányi
- Materials SciencePhysical Review X
- 3 May 2018
The success of first principles electronic structure calculation for predictive modeling in chemistry, solid state physics, and materials science is constrained by the limitations on simulated length…
Fractal-small-world dichotomy in real-world networks.
- Gábor Csányi, Balázs Szendrői
- Computer SciencePhysical review. E, Statistical, nonlinear, and…
- 3 June 2004
It is pointed out that the status of human social networks in this dichotomy of small-world networks exhibiting exponential neighborhood growth, and fractal-like networks, where neighborhoods grow according to a power law is far from clear.
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