Transferable Force Field Research Articles

A transferable classical force field to describe glyme based lithium solvate ionic liquids.

A non-polarizable force field for lithium (Li+) and bis(trifluoromethanesulfonyl)imide (TFSI-) ions solvated in diglyme at around 0.2 mol fraction salt concentration was developed based on abinitio molecular dynamics (AIMD) simulations and a modified polymer consistent force field model. A force-torque matching based scheme, in conjunction with a genetic algorithm, was used to determine the Lennard-Jones (LJ) parameters of the ion-ion and ion-solvent interactions. This force field includes a partial charge scaling factor and a scaling factor for the 1-4 interactions. The resulting force field successfully reproduces the radial distribution function of the AIMD simulations and shows better agreement compared to the unmodified force field. The new force field was then used to simulate salt solutions with glymes of increasing chain lengths and different salt concentrations. The comparison of the MD simulations, using the new force field, with experimental data at different salt concentrations and AIMD simulations on equimolar concentrations of the triglyme system demonstrates the transferability of the force field parameters to longer glymes and higher salt concentrations. Furthermore, the force field appears to reproduce the features of the experimental x-ray structure factors, suggesting accuracy beyond the first solvation shell, for equimolar salt solutions using both triglyme and tetraglyme as the solvent. Overall, the new force field was found to accurately reproduce the molecular descriptions of LiTFSI-glyme systems not only at various salt concentrations but also with glymes of different chain lengths. Thus, the new force field provides a useful and accurate tool to perform in silico studies of this family of systems at the atomistic level.

Self diffusion, viscosity, and surface tension estimations of amines by using AUA4 force field

Accurate and efficient molecular dynamic simulation of amine functional group is crucial for designing improved carbon dioxide absorption processes, as thermodynamic and transport properties can be concurrently calculated with molecular-level insight. The exploration of these properties has been limited, and all-atom force fields have struggled to cope with the dynamic and equilibrium characteristics of amines, especially monoethanolamine, which possess and exceptionally low self-diffusivity. In response to this situation, we propose using an anisotropic united atom (AUA4) force field, which strikes a balance between precision and performance allowing to predict the key transport and equilibrium properties involved in mass transfer units. A total of 7 amines were included in this study. Among the main results observed in this work, there was excellent recreation of density. The surface tension evidenced performance similar to other transferable force fields with a general positive deviation of 20%. Regarding transport properties, methylamine and linear alkylamines shows moderate uniform deviations. Trimethylamne, being the only one tertiary amine, exhibited higher deviations than the other amines. In contrast, excellent results were obtained for ET-DI, while significant deviations were found for MEA. Nevertheless, in light of previous studies on this alkanolamine, these deviations appear promising. Finally, we were able to achieve the best predictions of the currently available force fields for ethanolamine by increasing its torsional barriers. This allowed us to accurately reproduce its low self-diffusion and viscosity while maintaining its main structural features.

Derivation of Morse potential

The Morse potential is widely used in chemistry, physics, and material science to describe interatomic interactions. We show that the Morse potential can be derived from a simple screened charge model accounting for the shielding of the nuclear charge by electron density. In this model, the parameter governing the width of the potential is related to the average shielding strength and the exponents of the atomic orbitals used in electronic structure calculations. The parameter representing the bond dissociation energy of a diatomic molecule is derived by combining the quantum mechanical covalent and classical electrostatic interactions. Moreover, the revealed connection between the parameters of the Morse potential and Pauling’s bond order and electronegativity provides insight into the relation between the classical and quantum mechanical descriptions of chemical bonds. This derivation of the Morse potential from the atomic quantities is expected to contribute to the development of the new basis sets incorporating Slater-type orbitals for electronic structure calculations and the transferable reactive force fields for molecular dynamics simulations. Furthermore, this interpretation of the Morse potential helps to understand the relationships between widely used chemical concepts, including effective atomic charge, bond dissociation energy, electronegativity, and bond order.

Multiscale Modeling of Physical Properties of Nanoporous Frameworks: Predicting Mechanical, Thermal, and Adsorption Behavior.

ConspectusNanoporous frameworks are a large and diverse family of supramolecular materials, whose chemical building units (organic, inorganic, or both) are assembled into a 3D architecture with well-defined connectivity and topology, featuring intrinsic porosity. These materials play a key role in various industrial processes and applications, such as energy production and conversion, fluid separation, gas storage, water harvesting, and many more. The performance and suitability of nanoporous materials for each specific application are directly related to both their physical and chemical properties, and their determination is crucial for process engineering and optimization of performances. In this Account, we focus on some recent developments in the multiscale modeling of physical properties of nanoporous frameworks, highlighting the latest advances in three specific areas: mechanical properties, thermal properties, and adsorption.In the study of the mechanical behavior of nanoporous materials, the past few years have seen a rapid acceleration of research. For example, computational resources have been pooled to create a public large-scale database of elastic constants as part of the Materials Project initiative to accelerate innovation in materials research: those can serve as a basis for data-based discovery of materials with targeted properties, as well as the training of machine learning predictor models.The large-scale prediction of thermal behavior, in comparison, is not yet routinely performed at such a large scale. Tentative databases have been assembled at the DFT level on specific families of materials, such as zeolites, but prediction at larger scale currently requires the use of transferable classical force fields, whose accuracy can be limited.Finally, adsorption is naturally one of the most studied physical properties of nanoporous frameworks, as fluid separation or storage is often the primary target for these materials. We highlight the recent achievements and open challenges for adsorption prediction at a large scale, focusing in particular on the accuracy of computational models and the reliability of comparisons with experimental data available. We detail some recent methodological improvements in the prediction of adsorption-related properties: in particular, we describe the recent research efforts to go beyond the study of thermodynamic quantities (uptake, adsorption enthalpy, and thermodynamic selectivity) and predict transport properties using data-based methods and high-throughput computational schemes. Finally, we stress the importance of data-based methods of addressing all sources of uncertainty.The Account concludes with some perspectives about the latest developments and open questions in data-based approaches and the integration of computational and experimental data together in the materials discovery loop.

Transferable Anisotropic Mie Potential Force Field for Alkanediols.

The development of force fields for polyfunctional molecules, such as alkanediols, requires a careful account of different average intramolecular conformations for gas states compared to dense liquid states, where intra- and intermolecular hydrogen bonds compete. In the present work, the transferable anisotropic Mie (TAMie) potential is extended to 1,n-alkanediols. Using the convention that intramolecular nonbonded interactions up to and including the third neighbor are excluded, all force field parameters developed previously for 1-alcohols were transferred to 1,5-pentanediol and beyond, with good agreement with experimental phase equilibrium data. To obtain trans-gauche ratios of 1,2-ethanediol and 1,3-propanediol that are consistent with experimental results, the propensities for intra- and intermolecular hydrogen bonds had to be balanced. This was achieved by parameterizing the intramolecular dihedral energy functions governing the O-C-C-O and O-C-C-C angles while intramolecular charge-charge interactions were active. All partial charges belonging to a functional group are collected in a charge group and all interactions among two charge groups are evaluated even if they are separated by less than three bonds. With this approach, it is possible to apply the nonbonded parameters from 1-alcohols to alkanediols without further refinement. The agreement with experimental phase equilibrium and shear viscosity data is of similar quality as for the 1-alcohols and the trans-gauche ratio agrees with literature results from spectroscopic measurements and ab initio calculations.

A Fast, Convenient, Polarizable Electrostatic Model for Molecular Dynamics.

We present an efficient polarizable electrostatic model, utilizing typed, atom-centered polarizabilities and the fast direct approximation, designed for efficient use in molecular dynamics (MD) simulations. The model provides two convenient approaches for assigning partial charges in the context of atomic polarizabilities. One is a generalization of RESP, called RESP-dPol, and the other, AM1-BCC-dPol, is an adaptation of the widely used AM1-BCC method. Both are designed to accurately replicate gas-phase quantum mechanical electrostatic potentials. Benchmarks of this polarizable electrostatic model against gas-phase dipole moments, molecular polarizabilities, bulk liquid densities, and static dielectric constants of organic liquids show good agreement with the reference values. Of note, the model yields markedly more accurate dielectric constants of organic liquids, relative to a matched nonpolarizable force field. MD simulations with this method, which is currently parametrized for molecules containing elements C, N, O, and H, run only about 3.6-fold slower than fixed charge force fields, while simulations with the self-consistent mutual polarization average 4.5-fold slower. Our results suggest that RESP-dPol and AM1-BCC-dPol afford improved accuracy relative to fixed charge force fields and are good starting points for developing general, affordable, and transferable polarizable force fields. The software implementing these approaches has been designed to utilize the force field fitting frameworks developed and maintained by the Open Force Field Initiative, setting the stage for further exploration of this approach to polarizable force field development.

Molecular Simulation Studies on the Vapor-Liquid Equilibrium of CO2 + 3,3,3-Trifluoropropene (R1243zf) Binary Mixtures.

As fourth-generation refrigerants with great development prospects, hydrofluoroolefins (HFOs) can be mixed with other refrigerants, such as carbon dioxide (CO2), to form refrigerant mixtures with low global warming potential (GWP) and zero ozone depleting potential (ODP) while retaining the advantages of each component. Refrigerants can work together to achieve complementary benefits. Combinations of CO2 and HFOs can strengthen the thermodynamic properties of CO2 while inhibiting the flammability of HFOs. At present, relatively few studies have been conducted on the CO2 + 3,3,3-trifluoropropene (R1243zf) mixture. Besides experimental approaches, molecular simulation has grown in importance as a way to determine thermodynamic and transport properties in recent years. In this study, the Gibbs Ensemble Monte Carlo (GEMC) method was used to simulate the vapor-liquid equilibrium (VLE) properties of the CO2 + R1243zf binary mixture in the temperature range of 273.15 to 313.15 K, in which the three-site rigid TraPPE force field and a fully flexible transferable all-atom force field were selected to describe CO2 and R1243zf, respectively. By comparing the GEMC simulation results with the experimental data, it was found that the average deviation of pressure is 2.33%, the average deviation of liquid phase molar fraction Δx is 0.0099, and the mean deviation of gaseous phase molar fraction Δy is 0.0204. The simulation results accord well with the experimental data and the fitting data of the correlation model in the literature, indicating that the molecular models used for CO2 and R1243zf can reliably predict the VLE properties. Finally, the critical parameters of the mixture at a temperature of 313.15 K were predicted, and the radial distribution functions (RDFs) of pure R1243zf and the mixture at 273.15 K and 3.5 MPa were calculated and analyzed by molecular dynamics (MD) simulation.

Exploring inhomogeneous surfaces: Ti-rich SrTiO3(110) reconstructions via active learning.

The investigation of inhomogeneous surfaces, where various local structures coexist, is crucial for understanding interfaces of technological interest, yet it presents significant challenges. Here, we study the atomic configurations of the (2 × m) Ti-rich surfaces at (110)-oriented SrTiO3 by bringing together scanning tunneling microscopy and transferable neural-network force fields combined with evolutionary exploration. We leverage an active learning methodology to iteratively extend the training data as needed for different configurations. Training on only small well-known reconstructions, we are able to extrapolate to the complicated and diverse overlayers encountered in different regions of the inhomogeneous SrTiO3(110)-(2 × m) surface. Our machine-learning-backed approach generates several new candidate structures, in good agreement with experiment and verified using density functional theory. The approach could be extended to other complex metal oxides featuring large coexisting surface reconstructions.

PhyNEO: A Neural-Network-Enhanced Physics-Driven Force Field Development Workflow for Bulk Organic Molecule and Polymer Simulations.

An accurate, generalizable, and transferable force field plays a crucial role in the molecular dynamics simulations of organic polymers and biomolecules. Conventional empirical force fields often fail to capture precise intermolecular interactions due to their negligence of important physics, such as polarization, charge penetration, many-body dispersion, etc. Moreover, the parameterization of these force fields relies heavily on top-down fittings, limiting their transferabilities to new systems where the experimental data are often unavailable. To address these challenges, we introduce a general and fully ab initio force field construction strategy, named PhyNEO. It features a hybrid approach that combines both the physics-driven and the data-driven methods and is able to generate a bulk potential with chemical accuracy using only quantum chemistry data of very small clusters. Careful separations of long-/short-range interactions and nonbonding/bonding interactions are the key to the success of PhyNEO. By such a strategy, we mitigate the limitations of pure data-driven methods in long-range interactions, thus largely increasing the data efficiency and the scalability of machine learning models. The new approach is thoroughly tested on poly(ethylene oxide) and polyethylene glycol systems, giving superior accuracies in both microscopic and bulk properties compared to conventional force fields. This work thus offers a promising framework for the development of advanced force fields in a wide range of organic molecular systems.

Machine learning aided computational exploration of metal–organic frameworks with open Cu sites for the effective separation of hydrogen isotopes

Efficient separation of hydrogen isotopes is of vital importance to develop nuclear energy industry, while it remains a significant challenge to separate D2 from H2 due to their identical physicochemical properties. As one of the efficient alternatives to conventional techniques, the thermodynamic quantum sieving technology using metal–organic frameworks (MOFs) featuring open metal sites (OMSs) has shown a great potential. However, the lack of transferable force fields in conventional molecular simulations and high expense of brute-force screening hinder the quick discovery of MOFs targeted for D2/H2 separation. Herein, based on the established force field with high accuracy and transferability, machine learning and feature engineering are applied to address these challenges. Machine learning comprehensively assesses different descriptors that influence the separation performance of 929 experimentally-reported MOFs bearing Cu(II)-OMS. By employing the same metal nodes, new Cu MOF database (6,748 MOFs) is constructed, in which 45 hypothetical MOFs are firstly identified out through feature engineering that exhibiting high performance. Furthermore, grand canonical Monte Carlo simulations are performed on these MOFs, among which the optimal one exhibits comparable selectivity (36.9) and high adsorbent performance score (315.9) that surpasses the state-of-the-art materials do. This work not only presents a cost-effective approach firstly applying in the separation of hydrogen isotopes, but also provides experimental guidance for the design of high-performance adsorbents.

Development of a Transferable Force Field between Metal-Organic Framework and Its Polymorph.

Conventionally, force fields for specific metal-organic frameworks (MOFs) are derived from quantum chemical simulations, but this method can be computationally intensive, especially in cases for large MOF structures. In this work, we devise a methodology to reduce the force field derivation costs by replacing the original MOF with a smaller polymorphic structure, with the hypothesis that the force field parameters will be transferrable among chemically identical, polymorphic MOF structures. Specifically, we demonstrate this transferability in MOF-177 structure for H2O and NH3 gas molecules and show that the force field parameters derived from a smaller polymorphic MOF-177 can be used accurately to the original MOF-177 structure. This methodology can accelerate the development of force field parameters for large porous materials, in which computational costs for conventional methods are expensive.

Simple, near-universal relationships between bond lengths, strengths, and anharmonicities

Harmonic bond force constants and bond lengths are shown to generally obey the simple relationships, ke=ζ2Re−3 (hydrides) and ke=10ζ1/2Re−4 (all other bond types), where ζ is the reduced nuclear charge and Re is the equilibrium bond length. Equally simple power-law relationships are found for higher-order bond force constants. Although not spectroscopically accurate, these models are nonetheless of significant heuristic value for identifying strongly multireference states of diatomic molecules (including electronically coupled excited states ill-suited for inclusion in laser-cooling schemes), rationalizing the observed trends in vibrational frequencies for diatomics and/or local mode oscillators within molecules or complexes and estimating and/or validating covalent bonding parameters within molecular mechanics force fields. Particular advantages of our approach over other bond length-strength scaling relationships proposed in the literature include its simplicity and generality and its appropriate asymptotic behavior. Notably, the relationships derived in this work can be used to predict harmonic and higher-order force constant bonds between any pair of atoms in the Periodic Table (including transition metals and lanthanides) without requiring row- or column-dependent parameterization, to accuracies commensurate with conventional force field transferability errors. We therefore anticipate that they will expedite force field development for metal-containing complexes and materials, which are structurally well-characterized but challenging to parameterize ab initio.

Transferable Force Field for Gallium Nitride Crystal Growth from the Melt Using On-The-Fly Active Learning.

Atomic-scale simulations of reactive processes have been stymied by two factors: the lack of a suitable semiempirical force field on one hand and the impractically large computational burden of using ab initio molecular dynamics on the other hand. In this paper, we use an "on-the-fly" active learning technique to develop a nonparameterized force field that, in essence, exhibits the accuracy of density functional theory and the speed of a classical molecular dynamics simulation. We developed a force field capable of capturing the crystallization of gallium nitride (GaN) during a novel additive manufacturing process featuring the reaction of liquid Ga and gaseous nitrogen precursors to grow crystalline GaN thin films. We show that this machine learning model is capable of producing a single force field that can model solid, liquid, and gas phases involved in the process. We verified our computational predictions against a range of experimental measurements relevant to each phase and against ab initio calculations, showing that this nonparametric force field produces properties with excellent accuracy as well as exhibits computationally tractable efficiency. The force field is capable of allowing us to simulate the solid-liquid coexistence interface and the crystallization of GaN from the melt. The development of this transferable force field opens the opportunity to simulate the liquid-phase epitaxial growth more accurately than before to analyze reaction and diffusion processes and ultimately to establish a growth model of the additive manufacturing process to create the gallium nitride thin films.

Data scheme and data format for transferable force fields for molecular simulation

A generalized data scheme for transferable classical force fields used in molecular simulations, i.e. molecular dynamics and Monte Carlo simulation, is presented. The data scheme is implemented in an SQL-based data format. The data scheme and data format is machine readable, re-usable, and interoperable. A transferable force field is a chemical construction plan specifying intermolecular and intramolecular interactions between different types of atoms or different chemical groups and can be used for building a model for a given component. The data scheme proposed in this work (named TUK-FFDat) formalizes digitally these chemical construction plans, i.e. transferable force fields. It can be applied to all-atom as well as united-atom transferable force fields. The general applicability of the data scheme is demonstrated for different types of force fields (TraPPE, OPLS-AA, and Potoff). Furthermore, conversion tools for translating the data scheme between .xls spread sheet format and the SQL-based data format are provided. The data format can readily be integrated in existing workflows, simulation engines, and force field databases as well as for linking such.

Quantitative Simulations of Siloxane Adsorption in Metal-Organic Frameworks.

We present a transferable force field (FF) for simulating the bulk properties of linear and cyclic siloxanes and the adsorption of these species in metal-organic frameworks (MOFs). Unlike previous FFs for siloxanes, our FF accurately reproduces the vapor-liquid equilibria of each species in the bulk phase. The quality of our FF combined with the Universal Force Field using standard Lorentz-Berthelot combining rules for MOF atoms was assessed in a wide range of MOFs without open metal sites, showing good agreement with dispersion-corrected density functional theory calculations. Predictions with this FF show good agreement with the limited experimental data for siloxane adsorption in MOFs that is available. As an example of using the FF to predict adsorption properties in MOFs, we present simulations examining entropy effects in binary linear and cyclic siloxane mixture coadsorption in the large-pore MOF with structure code FOTNIN.

Accurate modelling of pyrrolidinium ionic liquids with charge and vdW scaling

Molecular simulation as a complementing tool to experimental measurements has grown to be an instrument with predictive power for ionic liquids (ILs) derivatives. The combination of molecule-specific atomic charges generated with ab initio calculations and bonded and vdW parameters extracted from pre-fitted transferable force fields serves as the common practice in this field. Charge scaling is often applied to account for charge transfer and polarization effects and the scaling factor 0.8 has been concluded as a near-optimal solution in many scientific reports based on detailed analyses of various structural and thermodynamic observables. However, for ILs from the pyrrolidinium family investigated in the current work, we show that the charge scaling treatment cannot really minimize the experiment-simulation deviations to an acceptable level, which calls for further parameter adjustment. Following the modelling protocol summarized in our previous works, we tune the vdW radius of ILs-forming ions with a 298 K density scan and secure a parameter set that satisfactorily reproduces not only the room-temperature properties but also the mass densities in a large temperature range from 288 K to 348 K. The slopes of the vdW-scaling profiles are between −1.9 g/cm3 and −1.6 g/cm3, which are similar to quinuclidinium ILs investigated in our previous work. This phenomenon suggests the similarity of responding behaviors of the two ILs family during vdW scaling, and this range of response strengths could possibly be representative for ILs vdW scaling. Aside from bulk solvents, we further consider the interaction of ILs with a series of external organic agents. Following the previously accumulated experience, a slightly modified vdW scaling factor is picked as an option balancing different interactions within the heterogeneous solution. Nonequilibrium alchemical free energy calculations are conducted to compute solvation free energies in ILs and also the distribution (partition) behaviors of solutes between aqueous and ILs phases. The computed physiochemical properties involving pyrrolidinium ILs agree well with the experimental reference. Another feature of the selected pyrrolidinium ILs is the systematic variation of the length of the alkyl substituting chain, which enables a direct investigation of the chain-length dependence of the prediction accuracies for both bulk density and solvation/partition thermodynamics. Interestingly, with the chain-length variation, the error of bulk density exhibits a monotonic response, while a fluctuating (non-monotonic) behavior is observed for prediction errors of solvation and partition thermodynamics, which is related to the diverse features of solute-solvent interactions. Therefore, the observed behaviors of the properties of bulk solvents cannot be generalized to the solvation/partition behaviors of the same solvent, and accurate reproductions of bulk behaviors do not guarantee accurate calculations of solvation and partition thermodynamics.

Toward Data-Driven Many-Body Simulations of Biomolecules in Solution: N-Methyl Acetamide as a Proxy for the Protein Backbone.

The development of molecular models with quantum-mechanical accuracy for predictive simulations of biomolecular systems has been a long-standing goal in the field of computational biophysics and biochemistry. As a first step toward a transferable force field for biomolecules entirely derived from "first-principles", we introduce a data-driven many-body energy (MB-nrg) potential energy function (PEF) for N-methylacetamide (NMA), a peptide bond capped by two methyl groups that is commonly used as a proxy for the protein backbone. The MB-nrg PEF is shown to accurately describe the energetics and structural properties of an isolated NMA molecule, including the normal modes of both cis and trans isomers and the energy variation along the isomerization path, as well as the multidimensional potential energy landscape of the NMA-H2O dimer in the gas phase. Importantly, we show that the MB-nrg PEF is fully transferable, enabling molecular dynamics simulations of NMA in solution with quantum-mechanical accuracy. Comparisons with results obtained with a popular pairwise-additive force field for biomolecules and a classical polarizable PEF demonstrate the ability of the MB-nrg PEF to accurately represent many-body effects in NMA-H2O interactions at both short and long distances, which is key to guaranteeing full transferability from the gas phase to the liquid phase.

Engineering Transferable Atomic Force Fields: Empirical Optimization of Hydrocarbon Lennard-Jones Interactions by Direct Mapping of Parameter Space.

The utility of atomistic simulations depends on the accuracy of the force field used to represent the potential energy landscape, the consistency with which interaction parameters can be assigned, and the extent to which parameters can be transferred between chemical entities. Here, parameter space mapping, a simple and robust procedure for atom typing (parameter assignment) and parameter optimization, is used to identify a minimal set of parameters capable of simultaneously reproducing the density, heat of vaporization, and solvation free energies for a targeted set of simple hydrocarbons. Using an atom-centered fixed charge model and a 6-12 Lennard-Jones potential, the experimental densities and the heats of vaporization for 22 hydrocarbons (linear, cyclic, and aromatic) could be predicted with high precision: average unsigned error (AUE) of 6.1 kg/m3 and 0.5 kJ/mol, respectively, and R2 values of 0.991 and 0.999, respectively. For the 17 compounds with experimental solvation free energy values in water, the AUE was 1.3 kJ/mol, and the slope and R2 for the line of best fit were 0.968 and 0.991, respectively. A key element in ensuring transferability in this work was minimizing confounding variables by ensuring that the calculation of observables was independent of the precise choice of simulation settings (cutoff, bond constraints, etc.) and the explicit consideration of correlations between parameters.

Ab Initio Derived Classical Force Field for Molecular Dynamics Simulations of ZnO Surfaces in Biological Environment.

Zinc oxide nanostructures are used in an ever increasing line of applications in technology and biomedical fields. This requires a detailed understanding of the phenomena that occur at the surface particularly in aqueous environments and in contact with biomolecules. In this work, we used ab initio molecular dynamics (AIMD) simulations to determine structural details of ZnO surfaces in water and to develop a general and transferable classical force field for hydrated ZnO surfaces. AIMD simulations show that water molecules dissociate near unmodified ZnO surfaces, forming hydroxyl groups at about 65% of the surface Zn atoms and protonating 3-coordinated surface oxygen atoms, while the rest of the surface Zn atoms bind molecularly adsorbed waters. Several force field atom types for ZnO surface atoms were identified by analysis of the specific connectivities of atoms. The analysis of the electron density was then used to determine partial charges and Lennard-Jones parameters for the identified force field atom types. The obtained force field was validated by comparison with AIMD results and with available experimental data on adsorption and immersion enthalpies, as well as adsorption free energies of several amino acids in methanol. The developed force field can be used for modeling of ZnO in aqueous and other fluid environments and in interaction with biomolecules.

Short-range order and its impacts on the BCC MoNbTaW multi-principal element alloy by the machine-learning potential

We utilize a machine-learning force field, trained by a neural network (NN) with bispectrum coefficients as descriptors, to investigate the chemical short-range order (SRO) influences on the BCC MoNbTaW alloy strengthening mechanism. The NN interatomic potential offers a transferable force field that exhibits accuracy comparable to density functional theory. This innovative NN potential is employed to examine the SRO effects on various aspects such as elasticity, vibrational modes, plasticity, and strength in the MoNbTaW multi-principal element alloy (MPEA). The findings reveal a significant attraction between Mo-Ta pairs, resulting in the formation of locally ordered B2 clusters. These clusters can be adjusted via temperature and enhanced by Nb content. The presence of SRO leads to an increase in high-frequency phonon modes and introduces additional lattice friction to dislocation motion. This approach facilitates efficient compositional screening, paving the way for computational-guided materials design of novel MPEAs with enhanced performance. Furthermore, it opens up avenues for tuning the mechanical properties through optimization of the processing parameters.