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Abstract The earlier integration of validated Lennard–Jones (LJ) potentials for 8 fcc metals into materials and biomolecular force fields has advanced multiple research fields, for example, metal–electrolyte interfaces, recognition of biomolecules, colloidal assembly of metal nanostructures, alloys, and catalysis. Here we introduce 12-6 and 9-6 LJ parameters for classical all-atom simulations of 10 further fcc metals (Ac, Ca (α), Ce (γ), Es (β), Fe (γ), Ir, Rh, Sr (α), Th (α), Yb (β)) and stainless steel. The parameters reproduce lattice constants, surface energies, water interfacial energies, and interactions with (bio)organic molecules in 0.1 to 5% agreement with experiment, as well as qualitative mechanical properties under standard conditions. Deviations are reduced up to a factor of one hundred in comparison to earlier Lennard–Jones parameters, embedded atom models, and density functional theory. We also explain a quantitative correlation between atomization energies from experiments and surface energies that supports parameter development. The models are computationally very efficient and applicable to an exponential space of alloys. Compatibility with a wide range of force fields such as the Interface force field (IFF), AMBER, CHARMM, COMPASS, CVFF, DREIDING, OPLS-AA, and PCFF enables reliable simulations of nanostructures up to millions of atoms and microsecond time scales. User-friendly model building and input generation are available in the CHARMM-GUI Nanomaterial Modeler. As a limitation, deviations in mechanical properties vary and are comparable to DFT methods. We discuss the incorporation of reactivity and features of the electronic structure to expand the range of applications and further increase the accuracy. 

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. 

Protein scaffolds direct the organization of amorphous precursors that transform into mineralized tissues, but the templating mechanism remains elusive. Motivated by models for the biomineralization of tooth enamel, wherein amyloid-like amelogenin nanoribbons guide the mineralization of apatite filaments, we investigated the impact of nanoribbon structure, sequence, and chemistry on amorphous calcium phosphate (ACP) nucleation. Using full-length human amelogenin and peptide analogs with an amyloid-like domain, films of β-sheet nanoribbons were self-assembled on graphite and characterized by in situ atomic force microscopy and molecular dynamics simulations. All sequences substantially reduce nucleation barriers for ACP by creating low-energy interfaces, while phosphoserines along the length of the nanoribbons dramatically enhance kinetic factors associated with ion binding. Furthermore, the distribution of negatively charged residues along the nanoribbons presents a potential match to the Ca–Ca distances of the multi-ion complexes that constitute ACP. These findings show that amyloid-like amelogenin nanoribbons provide potent scaffolds for ACP mineralization by presenting energetically and stereochemically favorable templates of calcium phosphate ion binding and suggest enhanced surface wetting toward calcium phosphates in general. 

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.