Accelerating the prediction of large carbon clusters via structure search: Evaluation of machine-learning and classical potentials

Karasulu, Bora; Leyssale, Jean‐Marc; Rowe, P.N.; Weber, Cédric; Tomás, Carla de

doi:10.1016/j.carbon.2022.01.031

Cited by 17 publications

(7 citation statements)

References 56 publications

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“…Our dataset consists of 546 independent MD trajectories describing the melt-quenching and thermal annealing of elemental carbon. The development of ML potentials for carbon 28,[31][32][33][34] and their application to scientic problems [35][36][37] have been widely documented in the literature, and the existence of established potentials such as C-GAP-17 (ref. 28) means that there is a direct route for creating synthetic data.…”

Section: Datasetmentioning

confidence: 99%

Synthetic data enable experiments in atomistic machine learning

2023

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Section: Datasetmentioning

confidence: 99%

Synthetic data enable experiments in atomistic machine learning

2023

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“…An extended family of existing and theoretically predicted carbon nanoclusters with a wide range of sizes (C n , n = 4 � 200) and ordered/disordered structures were collected (Figures 39 and 40), and their properties were compared in detail in the literature. [111] A non-closely related metallic carbon allotrope spiro-carbon (poly(spiro[2.2]penta-1,4-diyne, with I4 1 /amd (D 4h ) symmetry) can be represented by an interconnection of sp 3 -hybridized C atoms in a set of transcisoid-polyacetylene chains (Figuress 41 and 42). It possesses a lower relative energy than 1-diamondyne (Y-Carbon) and T-Carbon.…”

Section: Chemistryselectmentioning

confidence: 99%

Density Function Theory Predicted Carbon Allotropes: Recent Developments

et al. 2023

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The most recent data on experimentally undiscovered carbon allotropes, predicted by the Density Function Theory (DFT), are reviewed. Classic carbon allotropes, graphenes, carbon nanotubes, fullerenes and their hybrids are being studied using DFT and related methods, resulting a host of potentially existing forms based on 5‐, 6‐, 7‐member cycles and their distinct combinations. Also known are cyclocarbons and large carbon clusters, nanobelts and nanoribbons, liquid, metallic, semiconductive and superhard carbons, which could exist under high or low pressures. These carbon allotropes can contain C atoms in the same hybridization state or be as their mixture, for example sp2+sp3. For several carbon allotropes, possible synthesis methods, properties and applications are proposed.

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“…[22] EDIP is known to successfully predict topological properties of carbonecous films as well as clusters. [23,24] One of the first empirical models capable of providing an accurate description of low-tomedium pressure phases of carbon is the long-range carbon bond-order potential (LCBOP). The LCBOP model is partially based on ab initio data, closely matches the ab initio MD results for the liquid structure, and accounts for interplanar interactions in graphite.…”

Section: Introductionmentioning

confidence: 99%

“…[32] The performance of seven models in predicting the properties of carbon nanoclusters have been recently investigated, with a focus on their accuracy in structure search and global optimisations. [24] The GAP-20 model has emerged as the best performing model.…”

Section: Introductionmentioning

confidence: 99%

Exploring the configuration space of elemental carbon with empirical and machine learned interatomic potentials

Marchant

Miguel

Karasulu

et al. 2023

Preprint

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In the present work we detail how the many-body potential energy landscape of elemental carbon, as described by interatomic potentials, can be explored by utilising the nested sampling algorithm, allowing the calculation of their pressure-temperature phase diagram up to high pressures. We present a comparison of four interatomic potential models: Tersoff, EDIP, GAP-20 and its recently updated version, GAP-20U. Our evaluation is focused on their macroscopic properties, particularly on their melting transition and on identifying thermodynamically stable solid structures up to at least 100 GPa. The studied models all form graphite structures upon freezing at lower pressure, then the diamond structure as the pressure increases. In the cases of the Tersoff, EDIP and GAP-20U potentials we were able to locate the transition between these phases. We placed particular focus on the state-of-the-art machine-learned models, the GAP-20 and GAP-20U, and calculated their phase diagrams up to 1 and 0.1 TPa, respectively, to evaluate their predictive capabilities well outside of the models’ fitting range. The phase diagrams of the GAP models showed remarkably good agreement with the experimental phase diagram up to 200 GPa. However, we found that the accuracy of the models’ descriptions of graphite was adversely affected by the relative stability of phases with incorrect layer spacing. By adding a suitable selection of graphite structures to the GAP training set and re-training the model, we have derived an improved model — referred to as the GAP-20U+gr — that suppresses erroneous local minima in the graphitic energy landscape. At extreme high pressure nested sampling identified two novel stable solid structures in the GAP-20 model, a strained diamond structure above 270 GPa and a strained hexagonal-close-packed structure above 890 GPa. However, the stability of these two phases were not confirmed by DFT calculations, highlighting potential routes to further improve the GAP model.

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Accelerating the prediction of large carbon clusters via structure search: Evaluation of machine-learning and classical potentials

Cited by 17 publications

References 56 publications

Synthetic data enable experiments in atomistic machine learning

Synthetic data enable experiments in atomistic machine learning

Density Function Theory Predicted Carbon Allotropes: Recent Developments

Exploring the configuration space of elemental carbon with empirical and machine learned interatomic potentials

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