DOI: 10.5281/zenodo.17253021
title: Structural models of carbon-based materials generated with molecular augmented dynamics

description: These are atomic-scale models of disordered carbon-based materials generated using molecular augmented dynamics (MAD) using experimental constraints (X-ray diffraction, neutron diffraction, X-ray photoelectron spectroscopy) and a control simulation protocol based on regular molecular dynamics (MD) without the experimental constraints.

The materials include pure carbon forms:



tetrahedral amorphous carbon (ta-C);

glassy carbon;

nanoporous carbon;


as well as binary mixtures of carbon compounds:



deuterated/hydrogenated amorphous carbon (a-C:D, a-C:H);

oxygen-rich amorphous carbon (a-COx).


For a-C:H, experimental a-C:D data was used in the structure generation because of the difficulty in matching the available a-C:H data due to the non-trivial interaction of hydrogen's most common isotope (protium, 1H) with neutrons. Thus, available experimental data with deuterium (2H) was used instead. The XYZ structures labeled as "aCD" and "aCH" are the same, generated with target a-C:D data, with deuterium ("D") or hydrogen ("H") labels, respectively. This is to ensure the XYZ files can be imported with common atomistic manipulation software that might give errors with "D" labels.

The structures where generated with the TurboGAP code (turbogap.fi) using GAP potentials trained for pure-C [1], C-H mixtures [2], C-O mixtures [3] and a core-electron binding energy model trained for C-H-O [3, 4]. The methodology used is a modified Hamiltonian dynamics approach [3] whose extension to analytical forces is known as MAD, with all the details provided in the following pair of papers:



T. Zarrouk and M.A. Caro. Molecular augmented dynamics: Generating experimentally consistent atomistic structures by design. arXiv:2508.17132

T. Zarrouk and M.A. Caro. Linear-scaling calculation of experimental observables for molecular augmented dynamics simulations. arXiv:2509.22388


A gallery of the structures, plotted using ASE [5], Ovito [6] and ase_tools [7], is provided with file name "gallery.png". All other files use an obvious naming ocnvention.

The experimental data were obtained from the literature [8-12]. All the details are given in the reference papers [13, 14].

Contact

You can get in contact with either or both the authors at firstname.lastname(at)aalto.fi

Funding

The authors acknowledge financial support from the Research Council of Finland under projects 330488, 347252, 352484, 355301 and 364778, from the European Union’s M-ERA.NET 3 program (NACAB project under grant agreement No 958174). We acknowledge the EuroHPC Joint Undertaking for awarding this project (XCALE innovation study within the Inno4scale project under grant agreement No 101118139) access to the EuroHPC supercomputer LUMI, hosted by CSC (Finland) and the LUMI consortium through a EuroHPC Regular Access call. The authors also acknowledge other computational resources from CSC – the Finnish IT Center for Science and Aalto University’s Science-IT project.

References



H. Muhli, X. Chen, A. P. Bartók, P. Hernández-León, G. Csányi, T. Ala-Nissila, and M. A. Caro. Machine learning force fields based on local parametrization of dispersion interactions: Application to the phase diagram of C60. Phys. Rev. B 104, 054106 (2021).

R. Ibragimova, M. S. Kuklin, T. Zarrouk, and M. A. Caro. Unifying the Description of Hydrocarbons and Hydrogenated Carbon Materials with a Chemically Reactive Machine Learning Interatomic Potential. Chem. Mater. 37, 1094 (2025).

T. Zarrouk, R. Ibragimova, A. P. Bartók, and M. A. Caro. Experiment-driven atomistic materials modeling: A case study combining x-ray photoelectron spectroscopy and machine learning potentials to infer the structure of oxygen-rich amorphous carbon. J. Am. Chem. Soc. 146, 14645 (2024).

D. Golze, M. Hirvensalo, Hernández-León P., A. Aarva, J. Etula, T. Susi, P. Rinke, T. Laurila, and M. A. Caro. Accurate computational prediction of core-electron binding energies in carbon-based materials: A machine-learning model combining DFT and GW. Chem. Mater. 34, 6240 (2022).

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A. Stukowski. Visualization and analysis of atomistic simulation data with OVITO–the Open Visualization Tool. Modelling Simul. Mater. Sci. Eng. 18, 015012 (2009).

github.com/mcaroba/ase_tools

Z. Zeng, L. Yang, Q. Zeng, H. Lou, H. Sheng, J. Wen, D. J. Miller, Y. Meng, W. Yang, W. L. Mao, and H.-K. Mao. Synthesis of quenchable amorphous diamond. Nat. Commun. 8, 322 (2017).

K. W. R. Gilkes, P. H. Gaskell, and J. Robertson. Comparison of neutron-scattering data for tetrahedral amorphous carbon with structural models. Phys. Rev. B 51, 12303 (1995).

T. M. Burke, R. J. Newport, W. S. Howells, K. W. R. Gilkes, and P. H. Gaskell. The structure of a-C:H(D) by neutron diffraction and isotropic enrichment. J. Non-Cryst. Solids 164, 1139 (1993).

C. A. Santini, A. Sebastian, C. Marchiori, V. P. Jonnalagadda, L. Dellmann, W. W. Koelmans, M. D. Rossell, C. P. Rossel, and E. Eleftheriou. Oxygenated amorphous carbon for resistive memory applications. Nat. Commun. 6, 1 (2015).

A. C. Forse, C. Merlet, P. K. Allan, E. K. Humphreys, J. M. Griffin, M. Aslan, M. Zeiger, V. Presser, Y. Gogotsi, and C. P. Grey. New insights into the structure of nanoporous carbons from NMR, Raman, and pair distribution function analysis. Chem. Mater. 27, 6848 (2015).

T. Zarrouk and M. A. Caro. Molecular augmented dynamics: Generating experimentally consistent atomistic structures by design. arXiv:2508.17132.

T. Zarrouk and M. A. Caro. Linear-scaling calculation of experimental observables for molecular augmented dynamics simulations. arXiv:2509.22388.