Reactive molecular dynamics (Reactive Force Field, ReaxFF) simulations provide a powerful computational framework for modeling complex chemical reactions in nanomaterials. Unlike traditional molecular dynamics (MD), which relies on fixed bonding topologies, ReaxFF incorporates dynamic bond formation and breaking through bond-order potentials. This enables the study of reactive processes such as oxidation, catalytic growth, and degradation in nanoscale systems with atomic-level precision.

The foundation of ReaxFF lies in its bond-order formalism, which dynamically calculates bond strengths based on interatomic distances and local coordination. The bond-order concept originates from the work of Tersoff and Brenner, where the bond energy depends on the local environment rather than predefined connectivity. In ReaxFF, the bond order is computed as a continuous function of distance, allowing smooth transitions between bonded and non-bonded states. This approach captures bond dissociation, formation, and intermediate states during reactions.

Parameterization of ReaxFF involves fitting force field parameters to quantum mechanical data or experimental results. The potential energy function includes contributions from bond stretching, angle bending, torsion, van der Waals interactions, and Coulombic forces. Each term is designed to reproduce reaction energetics, transition states, and equilibrium geometries accurately. Training datasets typically include dissociation curves, reaction barriers, and bulk properties of reference materials. Transferability is ensured by calibrating parameters across multiple chemical environments.

A key application of ReaxFF is modeling oxidation processes in metallic or carbon-based nanoparticles. For instance, simulations of aluminum nanoparticle oxidation reveal the formation of an oxide shell that grows inward as oxygen diffuses through the lattice. The bond-order description captures the transition from metallic Al-Al bonds to Al-O bonds, including the exothermic energy release during oxidation. Similarly, graphene oxidation simulations show epoxy and hydroxyl group formation, leading to structural defects and eventual disintegration at high temperatures.

Catalytic growth of carbon nanotubes (CNTs) is another area where ReaxFF excels. The simulations elucidate the role of transition metal nanoparticles (e.g., Fe, Ni) in decomposing carbon precursors and facilitating CNT nucleation. The dynamic bond-breaking and reformation mechanisms allow researchers to study the interplay between catalyst morphology, carbon feedstock dissociation, and tube chirality. For example, simulations reveal that step edges on Ni nanoparticles serve as active sites for C-C bond formation, while subsurface carbon diffusion influences growth rates.

Polymer degradation under thermal or mechanical stress is also effectively modeled using ReaxFF. The scission of polymer chains, crosslinking reactions, and volatile byproduct formation can be tracked over time. In polyethylene degradation, simulations show that radical formation initiates chain breaking, followed by hydrogen abstraction and the release of small hydrocarbons. The bond-order potential captures the competition between chain fragmentation and recombination, providing insights into degradation pathways.

Non-reactive MD simulations, in contrast, lack the ability to model chemical transformations. Classical force fields (e.g., Lennard-Jones, CHARMM) assume fixed bonding patterns, making them unsuitable for processes involving bond rearrangement. While non-reactive MD is efficient for studying structural dynamics, diffusion, or mechanical properties in stable materials, it fails in scenarios such as combustion, corrosion, or catalytic reactions. For example, simulating CNT growth with non-reactive MD would require predefined bonding changes, missing the spontaneous bond formation observed in experiments.

ReaxFF bridges the gap between quantum mechanics and classical MD by offering a computationally tractable method for large-scale reactive systems. Quantum chemical methods (e.g., DFT) provide high accuracy but are limited to small systems and short timescales. ReaxFF, however, can simulate thousands of atoms over nanoseconds while retaining chemical fidelity. This makes it ideal for studying mesoscale phenomena like crack propagation in polymers, where bond rupture and radical chemistry play critical roles.

Despite its advantages, ReaxFF has limitations. The force field parameters must be carefully validated for each chemical system, and the computational cost is higher than non-reactive MD. Additionally, long-range charge transfer and certain electronic effects may require hybrid approaches. Nevertheless, its ability to model bond rearrangement in nanomaterials makes ReaxFF indispensable for understanding reaction mechanisms, optimizing synthesis routes, and predicting material stability under extreme conditions.

In summary, ReaxFF simulations enable detailed investigations of chemical reactions in nanomaterials through bond-order potentials and dynamic bonding descriptions. Applications span nanoparticle oxidation, catalytic growth of nanotubes, and polymer degradation, where traditional MD methods fall short. By combining computational efficiency with chemical accuracy, ReaxFF provides unique insights into nanomaterial reactivity, guiding experimental design and advancing nanotechnology applications.