The reactive force-field (ReaxFF) interatomic potential is a powerful computational tool for exploring, developing and optimizing material properties. Methods based on the principles of quantum mechanics (QM), while offering valuable theoretical guidance at the electronic level, are often too computationally intense for simulations that consider the full dynamic evolution of a system. Alternatively, empirical interatomic potentials that are based on classical principles require significantly fewer computational resources, which enables simulations to better describe dynamic processes over longer timeframes and on larger scales. Such methods, however, typically require a predefined connectivity between atoms, precluding simulations that involve reactive events. The ReaxFF method was developed to help bridge this gap. Approaching the gap from the classical side, ReaxFF casts the empirical interatomic potential within a bond-order formalism, thus implicitly describing chemical bonding without expensive QM calculations. This article provides an overview of the development, application, and future directions of the ReaxFF method.

Sulfur is a very promising cathode material for rechargeable energy storage devices. However, sulfur cathodes undergo a noticeable volume variation upon cycling, which induces mechanical stress. In spite of intensive investigation of the electrochemical behavior of the lithiated sulfur compounds, their mechanical properties are not very well understood. In order to fill this gap, we developed a ReaxFF interatomic potential to describe Li–S interactions and performed molecular dynamics (MD) simulations to study the structural, mechanical, and kinetic behavior of the amorphous lithiated sulfur (a-Li_xS) compounds. We examined the effect of lithiation on material properties such as ultimate strength, yield strength, and Young's modulus. Our results suggest that with increasing lithium content, the strength of lithiated sulfur compounds improves, although this increment is not linear with lithiation. The diffusion coefficients of both lithium and sulfur were computed for the a-Li_xS system at various stages of Li-loading. A grand canonical Monte Carlo (GCMC) scheme was used to calculate the open circuit voltage profile during cell discharge. The Li–S binary phase diagram was constructed using genetic algorithm based tools. Overall, these simulation results provide insight into the behavior of sulfur based cathode materials that are needed for developing lithium–sulfur batteries.

Lithium-sulfur batteries are amongst the most appealing choices for the next generation large-scale energy storage applications. However, these batteries still suffer several formidable performance degradation issues that impede its commercialization. The lithium negative electrode yields high anodic capacity, but it causes dendrite formation and raises safety concerns. Furthermore, the high reactivity of lithium is accountable for electrolyte decomposition. To investigate these issues and possible countermeasures, we used ReaxFF reactive molecular dynamics simulations to elucidate anode-electrolyte interfacial chemistry and utilized an ex-situ anode surface treatment with Teflon coating. In this study, we employed Li/SWCNT (single-wall carbon nanotube) composite anode instead of lithium metal and tetra(ethylene glycol) dimethyl ether (TEGDME) as electrolyte. We find that at lithium rich environment at the anode-electrolyte interface, electrolyte dissociates and generates ethylene gas as a major reaction product, while utilization of Teflon layer suppresses the lithium reactivity and reduces electrolyte decomposition. Lithium discharge from the negative electrode is an exothermic event that creates local hot spots at the interfacial region and expedites electrolyte dissociation reaction kinetics. Usage of Teflon dampens initial heat flow and effectively reduces lithium reactivity with the electrolyte.

Reactive force fields have enabled an atomic level description of a wide range of phenomena, from chemistry at extreme conditions to the operation of electrochemical devices and catalysis. While significant insight and semi-quantitative understanding have been drawn from such work, the accuracy of reactive force fields limits quantitative predictions. We developed a neural network reactive force field (NNRF) for CHNO systems to describe the decomposition and reaction of the high-energy nitramine 1,3,5-trinitroperhydro-1,3,5-triazine (RDX). NNRF was trained using energies and forces of a total of 3100 molecules (11,941 geometries) and 15 condensed matter systems (32,973 geometries) obtained from density functional theory calculations with semi-empirical corrections to dispersion interactions. The training set is generated via a semi-automated iterative procedure that enables refinement of the NNRF until a desired accuracy is attained. The root mean square (RMS) error of NNRF on a testing set of configurations describing the reaction of RDX is one order of magnitude lower than current state of the art potentials.