Molecular dynamics simulations have become an essential tool for investigating thermal runaway initiation and propagation in battery components at the atomic and molecular scale. These simulations provide insights into the fundamental mechanisms of exothermic reactions, phase transitions, and decomposition processes that occur in electrode materials, electrolytes, and their interfaces under thermal stress. The ability to track individual atomic motions and chemical transformations makes MD particularly valuable for studying the early stages of thermal runaway before macroscopic observations become possible.

The modeling of layered oxide cathodes such as lithium nickel manganese cobalt oxides requires careful parameterization of interatomic potentials to capture both structural stability and reactivity at elevated temperatures. Reactive force fields like ReaxFF have been successfully applied to simulate oxygen release from transition metal oxide lattices, a critical exothermic process in thermal runaway. Simulations show that oxygen vacancy formation begins at temperatures around 200-250°C in NMC materials, with the rate increasing nonlinearly as temperature rises. The released oxygen atoms interact with organic electrolyte components, initiating chain reactions that produce heat and gaseous byproducts.

For liquid electrolytes, MD simulations track the decomposition pathways of carbonate solvents like ethylene carbonate and dimethyl carbonate when exposed to heat or reactive oxygen species. The simulations reveal that solvent molecules undergo homolytic bond cleavage at temperatures exceeding 150°C, forming radical intermediates that participate in exothermic polymerization and combustion reactions. The decomposition kinetics show strong temperature dependence, with activation energies typically ranging between 0.5-1.5 eV for common electrolyte decomposition pathways. The formation of flammable gas products such as CO, CO2, and hydrocarbons is quantitatively tracked through reaction coordinates in the simulations.

At electrode-electrolyte interfaces, MD simulations demonstrate how exothermic reactions propagate through the formation of metastable intermediate phases. The simulations capture the growth of resistive surface layers and their catalytic effects on further decomposition reactions. Interface models show that thermal decomposition initiates at defect sites and grain boundaries before spreading to bulk regions. The heat generation rates calculated from these interfacial reactions typically range from 10-100 W/g depending on the specific materials and temperature conditions.

The temperature-dependent phase transitions in battery materials are particularly well-suited for MD investigation. Simulations of lithium metal oxides show that the crystalline structure undergoes gradual disordering before reaching a collapse threshold where oxygen loss becomes significant. The simulations quantify lattice expansion coefficients and predict the critical temperatures for phase transitions that correlate well with experimental observations. For graphite anodes, MD tracks the intercalation compound decomposition and the onset of exothermic reactions with electrolytes.

To bridge the gap between atomic-scale mechanisms and macroscopic thermal behavior, MD simulations are increasingly coupled with finite element methods. This multiscale approach uses MD-derived reaction kinetics and heat generation rates as input parameters for continuum-scale thermal models. The coupling enables prediction of temperature distributions and heat propagation through full battery cells while maintaining the fidelity of fundamental chemical processes. The combined simulations show how localized hot spots develop and propagate through different cell components, providing insights into thermal runaway mitigation strategies.

Validation against accelerating rate calorimetry data demonstrates the predictive capability of MD simulations. The simulated onset temperatures for exothermic reactions typically fall within 10-20°C of ARC measurements for common battery chemistries. The heat flow profiles from simulations also match the characteristic shapes observed in ARC experiments, including the sequence of decomposition events and their relative intensities. Discrepancies between simulation and experiment often point to areas where interatomic potentials or reaction pathways require refinement.

Recent advances in MD methodologies have improved the accuracy of thermal runaway simulations. Enhanced sampling techniques allow investigation of rare events like nucleation of decomposition products. Machine learning potentials enable simulations of larger systems over longer timescales while maintaining quantum-level accuracy. These developments are providing new insights into the complex interplay between chemistry, heat transfer, and mechanical stresses during thermal runaway.

The application of MD simulations to battery safety has led to several important findings. Simulations have identified specific crystal orientations more prone to oxygen release, guiding the development of more stable cathode materials. The atomic-scale visualization of electrolyte decomposition pathways has informed the design of more thermally stable electrolyte formulations. Interface simulations have revealed the critical role of surface coatings in suppressing exothermic reactions.

Challenges remain in extending MD simulations to cover the full duration of thermal runaway events and larger length scales. Current simulations typically track processes occurring over nanoseconds to microseconds, while thermal runaway develops over seconds to minutes in real systems. Continued improvements in computational power and simulation algorithms are addressing these limitations, enabling more comprehensive studies of thermal runaway from initiation to propagation.

The integration of MD simulations with other computational and experimental techniques forms a powerful approach to battery safety research. By combining atomic-scale insights with macroscopic measurements, researchers are developing more accurate predictive models for thermal runaway behavior. These models support the design of safer battery systems through material selection, cell engineering, and thermal management strategies. As simulation methodologies continue to advance, their role in understanding and preventing thermal runaway will grow increasingly important for battery development and safety assessment.