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Graphene-based nanostructures hold immense potential as strong and lightweight materials, however, their mechanical properties such as modulus and strength are difficult to fully exploit due to challenges in atomic-scale engineering. This study presents a database of over 2,000 pristine and defective nanoscale CNT bundles and other graphitic assemblies, inspired by microscopy, with associated stress–strain curves from reactive molecular dynamics (MD) simulations using the reactive INTERFACE force field (IFF-R). These 3D structures, containing up to 80,000 atoms, enable detailed analyses of structure-stiffness-failure relationships. By leveraging the database and physics- and chemistry-informed machine learning (ML), accurate predictions of elastic moduli and tensile strength are demonstrated at speeds 1,000 to 10,000 times faster than efficient MD simulations. Hierarchical Graph Neural Networks with Spatial Information (HS-GNNs) are introduced, which integrate chemistry knowledge. HS-GNNs as well as extreme gradient boosted trees (XGBoost) achieve forecasts of mechanical properties of arbitrary carbon nanostructures with only 3 to 6% mean relative error. The reliability equals experimental accuracy and is up to 20 times higher than other ML methods. Predictions maintain 8 to 18% accuracy for large CNT bundles, CNT junctions, and carbon fiber cross-sections outside the training distribution. The physics- and chemistry-informed HS-GNN works remarkably well for data outside the training range while XGBoost works well with limited training data inside the training range. The carbon nanostructure database is designed for integration with multimodal experimental and simulation data, scalable beyond 100 nm size, and extendable to chemically similar compounds and broader property ranges. The ML approaches have potential for applications in structural materials, nanoelectronics, and carbon-based catalysts.
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Implementing Reactivity in Molecular Dynamics Simulations with the Interface Force Field (IFF-R) and Other Harmonic Force Fields
The Interface force field (IFF) enables accurate simulations of bulk and interfacial properties of compounds and multiphase materials. However, the simulation of reactions and mechanical properties up to failure remains challenging and expensive. Here we introduce the Reactive Interface Force Field (IFF-R) to analyze bond breaking and failure of complex materials using molecular dynamics simulations. IFF-R uses a Morse potential instead of a harmonic potential as typically employed in molecular dynamics force fields to describe the bond energy, which can render any desired bond reactive by specification of the curve shape of the potential energy and the bond dissociation energy. This facile extension of IFF and other force fields that utilize a harmonic bond energy term allows the description of bond breaking without loss in functionality, accuracy, and speed. The method enables quantitative, on-the-fly computations of bond breaking and stress-strain curves up to failure in any material. We illustrate accurate predictions of mechanical behavior for a variety of material systems, including metals (iron), ceramics (carbon nanotubes), polymers (polyacrylonitrile and cellulose I\b{eta}), and include sample parameters for common bonds based on using experimental and high-level (MP2) quantum mechanical reference data. Computed structures, surface energies, elastic moduli, and tensile strengths are in excellent agreement with available experimental data. Non-reactive properties are shown to be essentially identical to IFF values. Computations are approximately 50 times faster than using ReaxFF and require only a single set of parameters. Compatibility of IFF and IFF-R with biomolecular force fields allows the quantitative analysis of the mechanics of proteins, DNA, and other biological molecules.
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- PAR ID:
- 10297187
- Date Published:
- Journal Name:
- ArXivorg
- ISSN:
- 2331-8422
- Page Range / eLocation ID:
- arXiv:2107.14418
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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