Optimization of the Reax force field for the lithium–oxygen system using a high fidelity charge model

Effect of Electric Fields on the Decomposition of Phosphate Esters

Phosphate esters decompose on metal surfaces and form protective polyphosphate films. For many applications, such as in lubricants for electric vehicles and wind turbines, an understanding of the effect of electric fields on molecular decomposition is urgently required. Experimental investigations have yielded contradictory results, with some suggesting that electric fields improve tribological performance, while others have reported the opposite effect. Here, we use nonequilibrium molecular dynamics (NEMD) simulations to study the decomposition of tri-n-butyl phosphate (TNBP) molecules nanoconfined between ferrous surfaces (iron and iron oxide) under electrostatic fields. The reactive force field (ReaxFF) method is used to model the effects of chemical bonding and molecular dissociation. We show that the charge transfer with the polarization current equalization (QTPIE) method gives more realistic behavior compared to the standard charge equilibration (QEq) method under applied electrostatic fields. The rate of TNBP decomposition via carbon-oxygen bond dissociation is faster in the nanoconfined systems than that in the bulk due to the catalytic action of the surfaces. In all cases, the application of an electric field accelerates TNBP decomposition. When electric fields are applied to the confined systems, the phosphate anions are pulled toward the surface with high electric potential, while the alkyl cations are pulled to the surface with lower potential, leading to asymmetric film growth. Analysis of the temperature- and electric field strength-dependent dissociation rate constants using the Arrhenius equation suggests that, on reactive iron surfaces, the increased reactivity under an applied electric field is driven mostly by an increase in the pre-exponential factor, which is linked to the number of molecule-surface collisions. Conversely, the accelerated decomposition of TNBP on iron oxide surfaces can be attributed to a reduction in the activation energy with increasing electric field strength. Single-molecule nudged-elastic band (NEB) calculations also show a linear reduction in the energy barrier for carbon-oxygen bond breaking with electric field strength, due to stabilization of the charged transition state. The simulation results are consistent with experimental observations of enhanced and asymmetric tribofilm growth under electrostatic fields.

Exploring the Potential of Hierarchical Zeolite-Templated Carbon Materials for High-Performance Li-O2 Batteries: Insights from Molecular Simulations

The commercialization of ultrahigh capacity lithium-oxygen (Li-O2) batteries is highly dependent on the cathode architecture, and a better understanding of its role in species transport and solid discharge product (i.e., Li2O2) formation is critical to improving the discharge capacity. Tailoring the pore size distribution in the cathode structure can enhance the ion mobility and increase the number of reaction sites to improve the formation of solid Li2O2. In this work, the potential of hierarchical zeolite-templated carbon (ZTC) structures as novel electrodes for Li-O2 batteries was investigated by using reactive force field molecular dynamics simulation (reaxFF-MD). Initially, 47 microporous zeolite-templated carbon morphologies were screened based on microporosity and specific area. Among them, four structures (i.e., RHO-, BEA-, MFI-, and FAU-ZTCs) were selected for further investigation including hierarchical features in their structures. Discharge product cluster analysis, self-diffusivities, and density number profiles of Li+, O2, and dimethyl sulfoxide (DMSO) electrolyte were obtained to find that the RHO-type ZTC exhibited enhanced mass transfer compared to conventional microporous ZTC (approximately 31% for O2, 44% for Li+, and 91% for DMSO) electrodes. This is due to the promoted formation of small-sized product clusters, creating more accessible sites for oxygen reduction reaction and mass transport. These findings indicate how hierarchical ZTC electrodes with micro- and mesopores can enhance the discharge performance of aprotic Li-O2 batteries, providing molecular insights into the underlying phenomena.

In silico characterization of nanoparticles

Nanoparticles (NPs) make for intriguing heterogeneous catalysts due to their large active surface area and excellent and often size-dependent catalytic properties that emerge from a multitude of chemically different surface reaction sites. NP catalysts are, in principle, also highly tunable: even small changes to the NP size or surface facet composition, doping with heteroatoms, or changes of the supporting material can significantly alter their physicochemical properties. Because synthesis of size- and shape-controlled NP catalysts is challenging, the ability to computationally predict the most favorable NP structures for a catalytic reaction of interest is an in-demand skill that can help accelerate and streamline the material optimization process. Fundamentally, simulations of NP model systems present unique challenges to computational scientists. Not only must considerable methodological hurdles be overcome in performing calculations with hundreds to thousands of atoms while retaining appropriate accuracy to be able to probe the desired properties. Also, the data generated by simulations of NPs are typically more complex than data from simulations of, for example, single crystal surface models, and therefore often require different data analysis strategies. To this end, the present work aims to review analytical methods and data analysis strategies that have proven useful in extracting thermodynamic trends from NP simulations.

Development of a Mg/O ReaxFF Potential to describe the Passivation Processes in Magnesium-Ion Batteries

One of the key challenges preventing the breakthrough of magnesium-ion batteries (MIB) is the formation of a passivating boundary layer at the Mg anode. To describe the initial steps of Mg anode degradation by O2 impurities, a Mg/O ReaxFF (force field for reactive systems) parameter set was developed capable of accurately modeling the bulk, surface, adsorption, and diffusion properties of metallic Mg and the salt MgO. It is shown that O2 immediately dissociates upon first contact with the Mg anode (modeled as Mg(0001), Mg(101 ‾ $\bar 1$ 0)A, and Mg(101 ‾ $\bar 1$ 1)), heating the surface to several 1000 K. The high temperature assists the further oxidation and forms a rock salt interphase intersected by several grain boundaries. Among the Mg surface terminations, Mg(101 ‾ $\bar 1$ 0)A is the most reactive, forming an MgO layer with a thickness of up to 25 Å. The trained force field can be used to model the ongoing reactions in Mg-air batteries but also to study the oxidation of magnesium metal in general.

Recent Advances for Improving the Accuracy, Transferability, and Efficiency of Reactive Force Fields

Reactive force fields provide an affordable model for simulating chemical reactions at a fraction of the cost of quantum mechanical approaches. However, classically accounting for chemical reactivity often comes at the expense of accuracy and transferability, while computational cost is still large relative to nonreactive force fields. In this Perspective, we summarize recent efforts for improving the performance of reactive force fields in these three areas with a focus on the ReaxFF theoretical model. To improve accuracy, we describe recent reformulations of charge equilibration schemes to overcome unphysical long-range charge transfer, new ReaxFF models that account for explicit electrons, and corrections for energy conservation issues of the ReaxFF model. To enhance transferability we also highlight new advances to include explicit treatment of electrons in the ReaxFF and hybrid nonreactive/reactive simulations that make it possible to model charge transfer, redox chemistry, and large systems such as reverse micelles within the framework of a reactive force field. To address the computational cost, we review recent work in extended Lagrangian schemes and matrix preconditioners for accelerating the charge equilibration method component of ReaxFF and improvements in its software performance in LAMMPS.

The DFT-ReaxFF Hybrid Reactive Dynamics Method with Application to the Reductive Decomposition Reaction of the TFSI and DOL Electrolyte at a Lithium-Metal Anode Surface

The high energy density and suitable operating voltage make rechargeable lithium ion batteries (LIBs) promising candidates to replace such conventional energy storage devices as nonrechargeable batteries. However, the large-scale commercialization of LIBs is impeded significantly by the degradation of the electrolyte, which reacts with the highly reactive lithium metal anode. Future improvement of the battery performance requires a knowledge of the reaction mechanism that is responsible for the degradation and formation of the solid-electrolyte interphase (SEI). In this work, we develop a hybrid computational scheme, Hybrid ab initio molecular dynamics combined with reactive force fields, denoted HAIR, to accelerate Quantum Mechanics-based reaction dynamics (QM-MD or AIMD, for ab initio RD) simulations. The HAIR scheme extends the time scale accessible to AIMD by a factor of 10 times through interspersing reactive force field (ReaxFF) simulations between the AIMD parts. This enables simulations of the initial chemical reactions of SEI formation, which may take 1 ns, far too long for AIMD. We apply the HAIR method to the bis(trifluoromethanesulfonyl)imide (TFSI) electrolyte in 1,3-dioxolane (DOL) solvent at the Li metal electrode, demonstrating that HAIR reproduces the initial reactions of the electrolyte (decomposition of TFSI) previously observed in AIMD simulation while also capturing solvent reactions (DOL) that initiate by ring-opening to form such stable products as CO, CH₂O, and C₂H₄, as observed experimentally. These results demonstrate that the HAIR scheme can significantly increase the time scale for reactive MD simulations while retaining the accuracy of AIMD simulations. This enables a full atomistic description of the formation and evolution of SEI.