Molecular dynamics and finite element analysis have become critical tools for simulating flame-retardant mechanisms in battery systems, particularly for understanding how additives interact with combustible electrolytes under thermal stress. These computational approaches enable researchers to probe atomic-scale reactions and macroscopic thermal propagation, bridging the gap between material chemistry and system-level safety performance.

At the molecular level, reactive force field (ReaxFF) simulations capture the dynamic processes of flame-retardant additives during thermal runaway. The parameterization of these simulations focuses on two key mechanisms: radical scavenging and char formation. For radical scavenging, the force field describes bond dissociation energies of phosphorus or halogen-based additives reacting with hydrocarbon radicals from decomposing carbonate electrolytes. Typical parameters include O-H bond strengths around 400-450 kJ/mol for phosphate additives and C-Br dissociation energies near 280 kJ/mol for brominated compounds. Char formation requires parameterization of cross-linking reactions between decomposed electrolyte fragments and additives, with emphasis on C-C and C-O bond formation kinetics. The simulations track the evolution of radical concentrations and the growth of carbonaceous networks under increasing temperature gradients.

Validation against thermogravimetric-mass spectrometry (TG-MS) data ensures the simulations accurately reproduce experimental decomposition pathways. For example, a well-parameterized ReaxFF simulation of trimethyl phosphate additive should show mass loss profiles matching TG data within 5% error between 200-400°C, while MS signals for PO· radicals should appear within the same temperature window as experimental detection. The simulation should also predict the correct ratio of gaseous products (CO₂, CH₄, C₂H₄) observed in MS spectra during electrolyte decomposition.

Finite element modeling complements molecular dynamics by simulating how flame-retardant mechanisms affect heat propagation at the cell level. The models incorporate temperature-dependent material properties including thermal conductivity, specific heat capacity, and heat generation rates from exothermic reactions. Key parameters include the activation energy for electrolyte decomposition (typically 90-120 kJ/mol for common carbonate mixtures) and the heat absorption capacity of additives during endothermic reactions. The models solve coupled heat transfer and chemical reaction equations across battery components, predicting how flame-retardant additives delay thermal runaway by both chemical and physical mechanisms.

Case studies demonstrate the predictive power of these approaches. A ReaxFF simulation of lithium bis(oxalato)borate additive in ethylene carbonate electrolyte correctly forecasted a 40°C increase in thermal runaway onset temperature compared to baseline electrolyte, later confirmed experimentally. The simulation revealed the additive's dual mechanism: borate radicals scavenging hydrogen atoms from decomposing solvent while simultaneously promoting early formation of a boron-rich char layer. Another study modeling triphenyl phosphate in nickel-rich NMC cells predicted a 30% reduction in maximum combustion temperature, matching large-scale abuse test results within 8% error.

The finite element approach has successfully explained why certain additive formulations perform better in pouch cells versus cylindrical formats. A model analyzing dimethyl methylphosphonate distribution showed how the additive's vapor pressure leads to uneven concentration gradients in cylindrical windings, creating localized weak points during thermal propagation. This explained experimental observations where the same additive provided 25% longer runaway delay in pouch cells.

Despite these successes, significant limitations remain in modeling complex electrolyte decomposition pathways. Current force fields struggle with simultaneous representation of organic carbonate fragmentation, transition metal dissolution from cathodes, and additive reactions occurring in parallel. The decomposition of LiPF₆ salt generates multiple radical species (PF₅, PF₃O, etc.) that interact with both solvents and additives, creating reaction networks too complex for full ReaxFF parameterization. Additionally, the stochastic nature of gas bubble formation and venting during thermal runaway presents challenges for deterministic finite element models.

Recent advances in machine learning are addressing some of these limitations. Neural network potentials trained on quantum chemistry datasets can accelerate reactive simulations while maintaining accuracy near density functional theory levels. Graph neural networks have been applied to predict optimal additive combinations by learning from existing experimental data on flame-retardant efficacy. These models can process structural features of hundreds of candidate molecules and output predicted performance metrics such as radical scavenging efficiency or char yield. One demonstrated application screened 150 potential phosphorus-containing additives, correctly identifying the top three performers later verified by experimental testing.

Machine learning also enhances finite element modeling through real-time parameter optimization. Adaptive algorithms can adjust thermal boundary conditions and material properties during simulation based on comparison with in-situ temperature measurements from abuse tests. This approach recently improved prediction accuracy for thermal runaway propagation speed in 21700 cells from ±15% to ±7% error compared to experimental data.

The integration of these computational methods is advancing flame-retardant formulation design. Multiscale modeling pipelines now combine ReaxFF simulations of molecular interactions with finite element predictions of cell-level behavior, enabling virtual testing of new additive concepts before synthesis. A recent industry study applied this approach to develop a fluorinated phosphate additive that demonstrated 50% better flame suppression than existing commercial options while maintaining cycle life within 5% of standard electrolyte performance.

Ongoing developments focus on improving the treatment of interface phenomena between additives and electrode materials. New force field parameters are being developed to model how flame-retardant molecules interact with silicon anode surfaces or high-voltage cathode interfaces. At the macroscopic scale, finite element models are incorporating more sophisticated representations of gas transport through porous electrodes during thermal events.

Validation remains critical for both approaches. Standardized protocols are emerging for comparing simulation outputs with multiple experimental techniques including differential scanning calorimetry, accelerating rate calorimetry, and combustion calorimetry. The most robust models demonstrate correlation coefficients above 0.9 for key safety metrics like time-to-ignition or maximum pressure generation across different cell formats and state-of-charge conditions.

These computational tools are reducing development cycles for safer battery systems while providing fundamental insights into flame-retardant mechanisms. As algorithms improve and computing power increases, the models will handle more complex scenarios including combined mechanical-thermal abuse conditions and the effects of aging on additive performance. The ultimate goal remains predictive simulation of complete battery failure scenarios with accuracy sufficient for regulatory certification.