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

Completely Multipolar Model as a General Framework for Many-Body Interactions as Illustrated for Water

We introduce a general framework for many-body force fields, the Completely Multipolar Model (CMM), that utilizes multipolar electrical moments modulated by exponential decay of electron density as a common functional form for all terms of an energy decomposition analysis of intermolecular interactions. With this common functional form, the CMM model establishes well-formulated damped tensors that reach the correct asymptotes at both long- and short-range while formally ensuring no short-range catastrophes. CMM describes the separable EDA terms of dispersion, exchange polarization, and Pauli repulsion with short-ranged anisotropy, polarization as intramolecular charge fluctuations and induced dipoles, while charge transfer describes explicit movement of charge between molecules, and naturally describes many-body charge transfer by coupling into the polarization equations. We also utilize a new one-body potential that accounts for intramolecular polarization by including an electric field-dependent correction to the Morse potential to ensure that CMM reproduces all physically relevant monomer properties including the dipole moment, molecular polarizability, and dipole and polarizability derivatives. The quality of CMM is illustrated through agreement of individual terms of the EDA and excellent extrapolation to energies and geometries of an extensive validation set of water cluster data.

Reaction Dynamics of CO2 Hydrogenation on Iron Catalysts Using ReaxFF Molecular Dynamics Simulation

The conversion of CO2 to hydrocarbons using catalysts is a promising route to utilize CO2 and produce more valuable chemicals in a sustainable manner. Recent studies have shown that iron-based catalysts perform well for the hydrogenation of CO2. While the hydrogenation reaction mechanism in the gas phase is straightforward, when catalyzed by iron it has been demonstrated to involve various chemical transformations, and the selectivity and conversion are strongly dependent on the particle size. To further investigate the dependence of the reactivity of iron catalysts on cluster size, we performed reactive molecular dynamics simulations using the ReaxFF force field (ReaxFF-MD) for iron nanoclusters of various sizes in a CO2 and H2-rich environment. We demonstrated that the homogeneous hydrogenation of CO2 was correctly described by this ReaxFF model. The dissociation mechanism of CO2 on the Fe4, Fe16 clusters, and the bcc(100) Fe slab agrees with previous DFT results. The ReaxFF-MD simulations suggest a strong dependence of reactivity on the cluster size, with the Fe4 cluster having the highest reactivity. We show that ReaxFF-MD provides a route to understand reaction mechanisms in these nonequilibrium reactive processes where fast processes and local minima are important.

The role of charge in microdroplet redox chemistry

In charged water microdroplets, which occur in nature or in the lab upon ultrasonication or in electrospray processes, the thermodynamics for reactive chemistry can be dramatically altered relative to the bulk phase. Here, we provide a theoretical basis for the observation of accelerated chemistry by simulating water droplets of increasing charge imbalance to create redox agents such as hydroxyl and hydrogen radicals and solvated electrons. We compute the hydration enthalpy of OH- and H+ that controls the electron transfer process, and the corresponding changes in vertical ionization energy and vertical electron affinity of the ions, to create OH• and H• reactive species. We find that at ~ 20 - 50% of the Rayleigh limit of droplet charge the hydration enthalpy of both OH- and H+ have decreased by >50 kcal/mol such that electron transfer becomes thermodynamically favorable, in correspondence with the more favorable vertical electron affinity of H+ and the lowered vertical ionization energy of OH-. We provide scaling arguments that show that the nanoscale calculations and conclusions extend to the experimental microdroplet length scale. The relevance of the droplet charge for chemical reactivity is illustrated for the formation of H2O2, and has clear implications for other redox reactions observed to occur with enhanced rates in microdroplets.

Transferable Water Potentials Using Equivariant Neural Networks

Machine learning interatomic potentials (MLIPs) have emerged as a technique that promises quantum theory accuracy for reduced cost. It has been proposed [J. Chem. Phys. 2023, 158, 084111] that MLIPs trained on solely liquid water data cannot accurately transfer to the vapor-liquid equilibrium while recovering the many-body decomposition (MBD) analysis of gas-phase water clusters. This suggests that MLIPs do not directly learn the physically correct interactions of water molecules, limiting transferability. In this work, we show that MLIPs using equivariant architecture and trained on 3200 liquid water structures reproduces liquid-phase water properties (e.g., density within 0.003 g/cm3 between 230 and 365 K), vapor-liquid equilibrium properties up to 550 K, the MBD analysis of gas-phase water cluster up to six-body interactions, and the relative energy and the vibrational density of states of ice phases. We show that potentials developed using equivariant MLIPs allow transferability for arbitrary phases of water that remain stable in nanosecond long simulations.

Reactive Force Field Development for Propane Dehydrogenation on Platinum Surfaces

Propane dehydrogenation (PDH) is an on-purpose catalytic technology to produce propylene from propane that operates at high temperatures, 773-973 K. Several key industry players have been active in developing new catalysts and processes with improved carbon footprint and economics, where Pt-based catalysts have played a central role. The optimization of these catalytic systems through computational and atomistic simulations requires large-scale models that account for their reactivity and dynamic properties. To address this challenge, we developed a new reactive ReaxFF force field (2023-Pt/C/H) that enables large-scale simulations of PDH reactions catalyzed on Pt surfaces. The optimization of force-field parameters relies on a large training set of density functional theory (DFT) calculations of Pt-catalyzed PDH mechanism, including geometries, adsorption and relative energies of reaction intermediates, and key C-H and C-C bond-breaking/forming reaction steps on the Pt(111) surface. The internal validation supports the accuracy of the developed 2023-Pt/C/H force-field parameters, resulting in mean absolute errors (MAE) against DFT data of 14 and 12 kJ mol-1 for relative energies of intermediates and energy barriers, respectively. We demonstrated the applicability of the 2023-Pt/C/H force field with reactive molecular dynamics simulations of propane on different Pt surface topologies and temperatures. The simulations successfully model the formation of propene in the gas phase as well as competitive, unproductive reactions such as deep dehydrogenation and C-C bond cleavage that produce H, C1 and C2 adsorbed species responsible of catalytic deactivation of Pt surface. Results show the following reactivity order: Pt(111) < Pt(100) < Pt(211), and that for the stepped Pt(211) surface, propane activation occurs on low-coordinated Pt atoms at the steps. The measured selectivity as a function of surface topology follows the same trend as activity, the Pt(211) facet being the most selective. The 2023-Pt/C/H reactive force field can also describe the increase of reactivity with the temperature. From these simulations, we were able to estimate the Arrhenius activation energy, 73 kJ mol-1, whose value is close to those reported experimentally for PDH catalyzed by large, supported Pt nanoparticles . The newly developed 2023-Pt/C/H reactive force field can be used in subsequent investigations of different Pt topologies and of collective effects such as temperature, propane pressure, or H surface coverage.

Mixtures Recomposition by Neural Nets: A Multidisciplinary Overview

Artificial Neural Networks (ANNs) are transforming how we understand chemical mixtures, providing an expressive view of the chemical space and multiscale processes. Their hybridization with physical knowledge can bridge the gap between predictivity and understanding of the underlying processes. This overview explores recent progress in ANNs, particularly their potential in the 'recomposition' of chemical mixtures. Graph-based representations reveal patterns among mixture components, and deep learning models excel in capturing complexity and symmetries when compared to traditional Quantitative Structure-Property Relationship models. Key components, such as Hamiltonian networks and convolution operations, play a central role in representing multiscale mixtures. The integration of ANNs with Chemical Reaction Networks and Physics-Informed Neural Networks for inverse chemical kinetic problems is also examined. The combination of sensors with ANNs shows promise in optical and biomimetic applications. A common ground is identified in the context of statistical physics, where ANN-based methods iteratively adapt their models by blending their initial states with training data. The concept of mixture recomposition unveils a reciprocal inspiration between ANNs and reactive mixtures, highlighting learning behaviors influenced by the training environment.

Unifying Charge-Flow Polarization Models

We show that several models where electric polarization in molecular systems is modeled by charge-flow between atoms can all be considered as different manifestations of a general underlying mathematical structure. The models can be classified according to whether they employ atomic or bond parameters and whether they employ atom/bond hardness or softness. We show that an ab initio calculated charge response kernel can be considered as the inverse screened Coulombic matrix projected onto the zero-charge subspace, and this may provide a method for deriving charge screening functions to be used in force fields. The analysis suggests that some models contain redundancies, and we argue that a parameterization of charge-flow models in terms of bond softness is preferable as it depends on local quantities and decay to zero upon bond dissociation, while bond hardness depends on global quantities and increases toward infinity upon bond dissociation.

Reactive Martini: Chemical Reactions in Coarse-Grained Molecular Dynamics Simulations

Chemical reactions are ubiquitous in both materials and the biophysical sciences. While coarse-grained (CG) molecular dynamics simulations are often needed to study the spatiotemporal scales present in these fields, chemical reactivity has not been explored thoroughly in CG models. In this work, a new approach to model chemical reactivity is presented for the widely used Martini CG Martini model. Employing tabulated potentials with a single extra particle for the angle dependence, the model provides a generic framework for capturing bonded topology changes using nonbonded interactions. As a first example application, the reactive model is used to study the macrocycle formation of benzene-1,3-dithiol molecules through the formation of disulfide bonds. We show that starting from monomers, macrocycles with sizes in agreement with experimental results are obtained using reactive Martini. Overall, our reactive Martini framework is general and can be easily extended to other systems. All of the required scripts and tutorials to explain its use are provided online.

Highly Altered State of Proton Transport in Acid Pools in Charged Reverse Micelles

Transport mechanisms of solvated protons of 1 M HCl acid pools, confined within reverse micelles (RMs) containing the negatively charged surfactant sodium bis(2-ethylhexyl) sulfosuccinate (NaAOT) or the positively charged cetyltrimethylammonium bromide (CTABr), are analyzed with reactive force field simulations to interpret dynamical signatures from TeraHertz absorption and dielectric relaxation spectroscopy. We find that the forward proton hopping events for NaAOT are further suppressed compared to a nonionic RM, while the Grotthuss mechanism ceases altogether for CTABr. We attribute the sluggish proton dynamics for both charged RMs as due to headgroup and counterion charges that expel hydronium and chloride ions from the interface and into the bulk interior, thereby increasing the pH of the acid pools relative to the nonionic RM. For charged NaAOT and CTABr RMs, the localization of hydronium near a counterion or conjugate base reduces the Eigen and Zundel configurations that enable forward hopping. Thus, localized oscillatory hopping dominates, an effect that is most extreme for CTABr in which the proton residence time increases dramatically such that even oscillatory hopping is slow.

Spontaneous Formation of Hydrogen Peroxide in Water Microdroplets

There is accumulating evidence that many chemical reactions are accelerated by several orders of magnitude in micrometer-sized aqueous or organic liquid droplets compared to their corresponding bulk liquid phase. However, the molecular origin of the enhanced rates remains unclear as in the case of spontaneous appearance of 1 μM hydrogen peroxide in water microdroplets. In this Letter, we consider the range of ionization energies and whether interfacial electric fields of a microdroplet can feasibly overcome the high energy step from hydroxide ions (OH^-) to hydroxyl radicals (OH^•) in a primary H₂O₂ mechanism. We find that the vertical ionization energies (VIEs) of partially solvated OH^- ions are greatly lowered relative to the average VIE in the bulk liquid, unlike the case of the Cl^- anion which shows no reduction in the VIEs regardless of solvation environment. Overall reduced hydrogen-bonding and undercoordination of OH^- are structural features that are more readily present at the air-water interface, where the energy scale for ionization can be matched by statistically probable electric field values.

M-Chem: a Modular Software Package for Molecular Simulation that Spans Scientific Domains

We present a new software package called M-Chem that is designed from scratch in C++ and parallelized on shared-memory multi-core architectures to facilitate efficient molecular simulations. Currently, M-Chem is a fast molecular dynamics (MD) engine that supports the evaluation of energies and forces from two-body to many-body all-atom potentials, reactive force fields, coarse-grained models, combined quantum mechanics molecular mechanics (QM/MM) models, and external force drivers from machine learning, augmented by algorithms that are focused on gains in computational simulation times. M-Chem also includes a range of standard simulation capabilities including thermostats, barostats, multi-timestepping, and periodic cells, as well as newer methods such as fast extended Lagrangians and high quality electrostatic potential generation. At present M-Chem is a developer friendly environment in which we encourage new software contributors from diverse fields to build their algorithms, models, and methods in our modular framework. The long-term objective of M-Chem is to create an interdisciplinary platform for computational methods with applications ranging from biomolecular simulations, reactive chemistry, to materials research.

Is an Inductive Effect Explicit Account Required for Atomic Charges Aimed at Use within the Force Fields?

Polarization and inductive effects are the concepts that have been widely used in qualitative and even quantitative descriptions of experimentally observed properties in chemistry. The polarization effect has proven to be important in cases of biomolecular modeling though still the vast majority of molecular simulations use the classical non-polarizable force fields. In the last few decades, a lot of effort has been put into promoting the polarization effect and incorporating it into modern force fields and charge calculation methods. In contrast, the inductive effect has not attracted such attention and is effectively absent in both classic and modern force fields. Thus, a question is whether this difference corresponds to the difference in the physical significance of the effects and their explicit account, or is an artifact that should be corrected in the next generation of force fields. The significance of the electronic effects is studied in this paper through the prism of performance of specific models for atomic charge calculation that take into explicit account a nested set of effects: the formal charge, the nearest neighbors, the inductive effect, and finally the model, which takes into account all effects, which are possible to account for using atomic charges. The specific choice for the methods is the following: formal charges, MMFF94 bond charge increments, Dynamic Electronegativity Relaxation (DENR), and RESP. We propose a special scheme for the separate estimation of each particular effect contribution. By pairwise comparing the residual molecular electrostatic potential (MEP) errors of those charge models (aimed at best reproducing the quantum chemical reference MEP), we sequentially revealed how the account of each effect contributes to the better-quality MEP reproduction. The following relative importance of effects was estimated; thus, the natural hierarchy of the effects was established. First, the account of formal charges is of primordial importance. Second, the nearest neighbors account is the next in significance. Third, the explicit account of inductive effect in empirical charge calculation schemes was shown to significantly─both qualitatively and quantitatively─improve the quality of MEP reproduction. Fourth, the contribution of polarization is indirectly assessed. Surprisingly, it is of the order of magnitude of the inductive effect even for the molecular systems, for which it is anticipated to be more significant. Finally, the relative importance of anisotropic effects in neutral molecules was additionally reviewed.

Computer-aided drug design, quantum-mechanical methods for biological problems

Quantum chemistry enables to study systems with chemical accuracy (<1 kcal/mol from experiment) but is restricted to a handful of atoms due to its computational expense. This has led to ongoing interest to optimize and simplify these methods while retaining accuracy. Implementing quantum mechanical (QM) methods on modern hardware such as multiple-GPUs is one example of how the field is optimizing performance. Multiscale approaches like the so-called QM/molecular mechanical method are gaining popularity in drug discovery because they focus the application of QM methods on the region of choice (e.g., the binding site), while using efficient MM models to represent less relevant areas. The creation of simplified QM methods is another example, including the use of machine learning to create ultra-fast and accurate QM models. Herein, we summarize recent advancements in the development of optimized QM methods that enhance our ability to use these methods in computer aided drug discovery.

DFT-ReaxFF hybrid molecular dynamics investigation of the decomposition effects of localized high-concentration electrolyte in lithium metal batteries: LiFSI/DME/TFEO

Due to its low electrochemical potential and high theoretical specific energy, lithium-metal batteries (LMBs) have been considered as a promising advanced energy storage system for portable applications such as electric vehicles (EVs). However, the uncontrolled growth of lithium dendrites during cycling has remained a challenge. By utilizing an inert solvent to "dilute" the high concentration electrolytes, the concept of localized high-concentration electrolytes (LHCEs) has recently been demostrated as an effective solution to enable the dendrite-free cycling of LMBs. In this work, we investigated the reactions of 2 M lithium bis(fluorosulfonyl)imide (LiFSI) in a mixture of dimethoxyethane (DME)/tris(2,2,2-trifluoroethyl) orthoformate (TFEO) electrolyte at a Li metal anode. The SEI formation mechanism is investigated using a hybrid ab initio and reactive force field (HAIR) method. The 1n reactive HAIR trajectory reveals the important initial reduction reactions of LiFSI, TFEO, and DME. Particularly, both FSI anions and TFEO decompose quickly to release a considerable amount of F^-, which leads to a LiF-rich SEI inorganic inner layer (IIL). Furthermore, TFEO produces a significant amount of unsaturated carbon products, such as thiophene, which can potentially increase the conductivity of SEI to increase the battery performance. Meanwhile, XPS analysis is utilized to further investigate the evolution of the atomic environment in SEI. Future designs of better electrolytes can be greatly aided by these results.

Atomistic-Level Description of the Covalent Inhibition of SARS-CoV-2 Papain-like Protease

Inhibition of the papain-like protease (PLpro) of SARS-CoV-2 has been demonstrated to be a successful target to prevent the spreading of the coronavirus in the infected body. In this regard, covalent inhibitors, such as the recently proposed VIR251 ligand, can irreversibly inactivate PLpro by forming a covalent bond with a specific residue of the catalytic site (Cys111), through a Michael addition reaction. An inhibition mechanism can therefore be proposed, including four steps: (i) ligand entry into the protease pocket; (ii) Cys111 deprotonation of the thiol group by a Brønsted-Lowry base; (iii) Cys111-S- addition to the ligand; and (iv) proton transfer from the protonated base to the covalently bound ligand. Evaluating the energetics and PLpro conformational changes at each of these steps could aid the design of more efficient and selective covalent inhibitors. For this aim, we have studied by means of MD simulations and QM/MM calculations the whole mechanism. Regarding the first step, we show that the inhibitor entry in the PLpro pocket is thermodynamically favorable only when considering the neutral Cys111, that is, prior to the Cys111 deprotonation. For the second step, MD simulations revealed that His272 would deprotonate Cys111 after overcoming an energy barrier of ca. 32 kcal/mol (at the QM/MM level), but implying a decrease of the inhibitor stability inside the protease pocket. This information points to a reversible Cys111 deprotonation, whose equilibrium is largely shifted toward the neutral Cys111 form. Although thermodynamically disfavored, if Cys111 is deprotonated in close proximity to the vinylic carbon of the ligand, then covalent binding takes place in an irreversible way (third step) to form the enolate intermediate. Finally, due to Cys111-S- negative charge redistribution over the bound ligand, proton transfer from the initially protonated His272 is favored, finally leading to an irreversibly modified Cys111 and a restored His272. These results elucidate the selectivity of Cys111 to enable formation of a covalent bond, even if a weak proton acceptor is available, as His272.

Valence energy correction for electron reactive force field

Reactive force fields (ReaxFF) are a classical method to describe material properties based on a bond-order formalism, that allows bond dissociation and consequently investigations of reactive systems. Semiclassical treatment of electrons was introduced within ReaxFF simulations, better known as electron reactive force fields (eReaxFF), to explicitly treat electrons as spherical Gaussian waves. In the original version of eReaxFF, the electrons and electron-holes can lead to changes in both the bond energy and the Coulomb energy of the system. In the present study, the method was modified to allow an electron to modify the valence energy, therefore, permitting that the electron's presence modifies the three-body interactions, affecting the angle among three atoms. When a reaction path involving electron transfer is more sensitive to the geometric configuration of the molecules, corrections in the angular structure in the presence of electrons become more relevant; in this case, bond dissociation may not be enough to describe a reaction path. Consequently, the application of the extended eReaxFF method developed in this work should provide an improved description of a reaction path. As a first demonstration this semiclassical force field was parametrized for hydrogen and oxygen interactions, including water and water's ions. With the modified methodology both the overall accuracy of the force field but also the description of the angles within the molecules in presence of electrons could be improved.

CARNOT: a Fragment-Based Direct Molecular Dynamics and Virtual-Reality Simulation Package for Reactive Systems

Traditionally, the study of reaction mechanisms of complex reaction systems such as combustion has been performed on an individual basis by optimizations of transition structure and minimum energy path or by reaction dynamics trajectory calculations for one elementary reaction at a time. It is effective, but time-consuming, whereas important and unexpected processes could have been missed. In this article, we present a direct molecular dynamics (DMD) approach and a virtual-reality simulation program, CARNOT, in which plausible chemical reactions are simulated simultaneously at finite temperature and pressure conditions. A key concept of the present ab initio molecular dynamics method is to partition a large, chemically reactive system into molecular fragments that can be adjusted on the fly of a DMD simulation. The theory represents an extension of the explicit polarization method to reactive events, called ReX-Pol. We propose a highest-and-lowest adapted-spin approximation to define the local spins of individual fragments, rather than treating the entire system by a delocalized wave function. Consequently, the present ab initio DMD can be applied to reactive systems consisting of an arbitrarily varying number of closed and open-shell fragments such as free radicals, zwitterions, and separate ions found in combustion and other reactions. A graph-data structure algorithm was incorporated in CARNOT for the analysis of reaction networks, suitable for reaction mechanism reduction. Employing the PW91 density functional theory and the 6-31+G(d) basis set, the capabilities of the CARNOT program were illustrated by a combustion reaction, consisting of 28 650 atoms, and by reaction network analysis that revealed a range of mechanistic and dynamical events. The method may be useful for applications to other types of complex reactions.

Using atomic charges to model molecular polarization

We review different models for introducing electric polarization in force fields, with special focus on methods where polarization is modelled at the atomic charge level. While electric polarization has been included in several force fields, the common approach has been to focus on atomic dipole polarizability. Several approaches allow modelling electric polarization by using charge-flow between charge sites instead, but this has been less exploited, despite that atomic charges and charge-flow is expected to be more important than atomic dipoles and dipole polarizability. A number of challenges are required to be solved for charge-flow models to be incorporated into polarizable force fields, for example how to parameterize the models and how to make them computational efficient.

Can electric fields drive chemistry for an aqueous microdroplet?

Reaction rates of common organic reactions have been reported to increase by one to six orders of magnitude in aqueous microdroplets compared to bulk solution, but the reasons for the rate acceleration are poorly understood. Using a coarse-grained electron model that describes structural organization and electron densities for water droplets without the expense of ab initio methods, we investigate the electric field distributions at the air-water interface to understand the origin of surface reactivity. We find that electric field alignments along free O-H bonds at the surface are ~16 MV/cm larger on average than that found for O-H bonds in the interior of the water droplet. Furthermore, electric field distributions can be an order of magnitude larger than the average due to non-linear coupling of intramolecular solvent polarization with intermolecular solvent modes which may contribute to even greater surface reactivity for weakening or breaking chemical bonds at the droplet surface.

Progress towards machine learning reaction rate constants

Quantum and classical reaction rate constant calculations come at the cost of exploring potential energy surfaces. Due to the "curse of dimensionality", their evaluation quickly becomes unfeasible as the system size grows. Machine learning algorithms can accelerate the calculation of reaction rate constants by predicting them using low cost input features. In this perspective, we briefly introduce supervised machine learning algorithms in the context of reaction rate constant prediction. We discuss existing and recently created kinetic datasets and input feature representations as well as the use and design of machine learning algorithms to predict reaction rate constants or quantities required for their computation. Amongst these, we first describe the use of machine learning to predict activation, reaction, solvation and dissociation energies. We then look at the use of machine learning to predict reactive force field parameters, reaction rate constants as well as to help accelerate the search for minimum energy paths. Lastly, we provide an outlook on areas which have yet to be explored so as to improve and evaluate the use of machine learning algorithms for chemical reaction rate constants.

Water in an External Electric Field: Comparing Charge Distribution Methods Using ReaxFF Simulations

The growing interest in the effects of external electric fields on reactive processes requires predictive methods that can reach longer length and time scales than quantum mechanical simulations. Recently, many studies have included electric fields in ReaxFF, a widely used reactive molecular dynamics method. In the case of modeling an external electric field, the charge distribution method used in ReaxFF is critical. The most common charge distribution method used in previous studies of electric fields is the charge equilibration (QEq) method, which assumes that the system is a contiguous conductor and that charge transfer can occur across any distance. In contrast, many systems of interest are insulators or semiconductors, and long-distance charge transfer should not occur in response to a small difference in potential. This study focuses on the limitations of the QEq method in the context of water in an external electric field. We demonstrate that QEq can predict unphysical charge distributions and exhibits properties that do not converge as a function of system size. Furthermore, we show that electric fields within the recently developed atom-condensed Kohn-Sham density functional theory (DFT) approximated to the second-order (ACKS2) approach address the major limitations of electric fields in QEq. With ACKS2, we observe more physical charge distributions and properties that converge as a function of system size. We do not suggest that ACKS2 is perfect in all circumstances but rather show specific cases where it addresses the major shortcomings of QEq in the context of an external electric field.

Using C-DFT to develop an e-ReaxFF force field for acetophenone radical anion

Increased electricity usage over the past several decades has accelerated the need for efficient high-voltage power transmission with reliable insulating materials. Cross-linked polyethylene (XLPE) prepared via dicumyl peroxide (DCP) cross-linking has emerged as the insulator of choice for modern power cables. Although DCP cross-linking generates the desired XLPE product in high yield, other by-products are also produced. One such by-product, acetophenone, is particularly intriguing due to its aromaticity and positive electron affinity. In this work, constrained density functional theory (C-DFT) was utilized to develop an e-ReaxFF force field suitable for describing the acetophenone radical anion. Initial parameters were taken from the 2021 Akbarian e-ReaxFF force field, which was developed to describe XLPE chemistry. Then, C-DFT geometry optimizations were performed wherein an excess electron was constrained to each atom of acetophenone. The resulting C-DFT energy values for the various electronic positions were added to the e-ReaxFF training set. Next, an analogous set of structures was energy-minimized using e-ReaxFF, and equilibrium mixture compositions for the two methods were compared at multiple temperatures. Iterative fitting against C-DFT energy data was performed until satisfactory agreement was achieved. To test force field performance, molecular dynamics simulations were performed in e-ReaxFF and the resulting electronic distributions were qualitatively compared to unconstrained-DFT spin density data. By expanding our e-ReaxFF force field for XLPE, namely, adding the capability to describe acetophenone and its interactions with an excess electron, we move one step closer to a comprehensive molecular understanding of XLPE chemistry in a high-voltage power cable.

DL_FFLUX: A Parallel, Quantum Chemical Topology Force Field

DL_FFLUX is a force field based on quantum chemical topology that can perform molecular dynamics for flexible molecules endowed with polarizable atomic multipole moments (up to hexadecapole). Using the machine learning method kriging (aka Gaussian process regression), DL_FFLUX has access to atomic properties (energy, charge, dipole moment, etc.) with quantum mechanical accuracy. Newly optimized and parallelized using domain decomposition Message Passing Interface (MPI), DL_FFLUX is now able to deliver this rigorous methodology at scale while still in reasonable time frames. DL_FFLUX is delivered as an add-on to the widely distributed molecular dynamics code DL_POLY 4.08. For the systems studied here (10³–10⁵ atoms), DL_FFLUX is shown to add minimal computational cost to the standard DL_POLY package. In fact, the optimization of the electrostatics in DL_FFLUX means that, when high-rank multipole moments are enabled, DL_FFLUX is up to 1.25× faster than standard DL_POLY. The parallel DL_FFLUX preserves the quality of the scaling of MPI implementation in standard DL_POLY. For the first time, it is feasible to use the full capability of DL_FFLUX to study systems that are large enough to be of real-world interest. For example, a fully flexible, high-rank polarized (up to and including quadrupole moments) 1 ns simulation of a system of 10 125 atoms (3375 water molecules) takes 30 h (wall time) on 18 cores.