3 results found
Muller E, Schran C, Thiemann F, et al., 2021, Machine learning potentials for complex aqueous systems made simple, Proceedings of the National Academy of Sciences of USA, Vol: 38, Pages: 1-8, ISSN: 0027-8424
Simulation techniques based on accurate and efficient representations of potential energy surfaces are urgently needed for the understanding of complex aqueous systems such as solid-liquid interfaces. Here, we present a machine learning framework that enablesthe efficient development and validation of models for complex aqueous systems. Instead of trying to deliver a globally-optimal machinelearning potential, we propose to develop models applicable to specific thermodynamic state points in a simple and user-friendly process. After an initial ab initio simulation, a machine learning potential is constructed with minimum human effort through a data-drivenactive learning protocol. Such models can afterwards be applied inexhaustive simulations to provide reliable answers for the scientificquestion at hand, or systematically explore the thermal performanceof ab initio methods. We showcase this methodology on a diverseset of aqueous systems comprising bulk water with different ionsin solution, water on a titanium dioxide surface, as well as waterconfined in nanotubes and between molybdenum disulfide sheets.Highlighting the accuracy of our approach with respect to the underlying ab initio reference, the resulting models are evaluated in detailwith an automated validation protocol that includes structural anddynamical properties and the precision of the force prediction of themodels. Finally, we demonstrate the capabilities of our approach forthe description of water on the rutile titanium dioxide (110) surface toanalyze the structure and mobility of water on this surface. Such machine learning models provide a straightforward and uncomplicatedbut accurate extension of simulation time and length scales for complex systems.
Graphene’s intrinsically corrugated and wrinkled topology fundamentally influences its electronic, mechanical, and chemical properties. Experimental techniques allow the manipulation of pristine graphene and the controlled production of defects which allows one to control the atomic out-of-plane fluctuations and thus tune graphene’s properties. Here, we perform large scale machine learning-driven molecular dynamics simulations to understand the impact of defects on the structure of graphene. We find that defects cause significantly higher corrugation leading to a strongly wrinkled surface. The magnitude of this structural transformation strongly depends on the defect concentration and specific type of defect. Analyzing the atomic neighborhood of the defects reveals that the extent of these morphological changes depends on the preferred geometrical orientation and the interactions between defects. While our work highlights that defects can strongly affect graphene’s morphology, it also emphasizes the differences between distinct types by linking the global structure to the local environment of the defects.
Thiemann F, Rowe P, Muller E, et al., 2020, A machine learning potential for hexagonal boron nitride applied to thermally and mechanically induced rippling, The Journal of Physical Chemistry C: Energy Conversion and Storage, Optical and Electronic Devices, Interfaces, Nanomaterials, and Hard Matter, Vol: 124, Pages: 22278-22290, ISSN: 1932-7447
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].
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