Imperial College London
Alternate

Professor Erich A. Muller

Faculty of Engineering, Department of Chemical Engineering

Professor of Thermodynamics
 
 
 
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Contact

 

+44 (0)20 7594 1569e.muller Website

 
 
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Assistant

 

Mrs Raluca Reynolds +44 (0)20 7594 5557

 
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Location

 

409ACE ExtensionSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Thiemann:2020:10.1021/acs.jpcc.0c05831,
author = {Thiemann, F and Rowe, P and Muller, E and Michaelides, A},
doi = {10.1021/acs.jpcc.0c05831},
journal = {The Journal of Physical Chemistry C: Energy Conversion and Storage, Optical and Electronic Devices, Interfaces, Nanomaterials, and Hard Matter},
pages = {22278--22290},
title = {A machine learning potential for hexagonal boron nitride applied to thermally and mechanically induced rippling},
url = {http://dx.doi.org/10.1021/acs.jpcc.0c05831},
volume = {124},
year = {2020}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - We introduce an interatomic potential for hexagonal boron nitride (hBN) based on the Gaussian approximation potential (GAP) machine learning methodology. The potential is based on a training set of configurations collected from density functional theory (DFT) simulations and is capable of treating bulk and multilayer hBN as well as nanotubes of arbitrary chirality. The developed force field faithfully reproduces the potential energy surface predicted by DFT while improving the efficiency by several orders of magnitude. We test our potential by comparing formation energies, geometrical properties, phonon dispersion spectra, and mechanical properties with respect to benchmark DFT calculations and experiments. In addition, we use our model and a recently developed graphene-GAP to analyze and compare thermally and mechanically induced rippling in large scale two-dimensional (2D) hBN and graphene. Both materials show almost identical scaling behavior with an exponent of η ≈ 0.85 for the height fluctuations agreeing well with the theory of flexible membranes. On the basis of its lower resistance to bending, however, hBN experiences slightly larger out-of-plane deviations both at zero and finite applied external strain. Upon compression, a phase transition from incoherent ripple motion to soliton-ripples is observed for both materials. Our potential is freely available online at [http://www.libatoms.org].
AU  - Thiemann,F
AU  - Rowe,P
AU  - Muller,E
AU  - Michaelides,A
DO  - 10.1021/acs.jpcc.0c05831
EP  - 22290
PY  - 2020///
SN  - 1932-7447
SP  - 22278
TI  - A machine learning potential for hexagonal boron nitride applied to thermally and mechanically induced rippling
T2  - The Journal of Physical Chemistry C: Energy Conversion and Storage, Optical and Electronic Devices, Interfaces, Nanomaterials, and Hard Matter
UR  - http://dx.doi.org/10.1021/acs.jpcc.0c05831
UR  - http://hdl.handle.net/10044/1/83516
VL  - 124
ER  -
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