Set Of Force Field Parameters Research Articles

Unveiling the Borohydride Ion through Force-Field Development.

The borohydride ion, BH4-, is an essential reducing agent in many technological processes, yet its full understanding has been elusive, because of at least two significant challenges. One challenge arises from its marginal stability in aqueous solutions outside of basic pH conditions, which considerably limits the experimental thermodynamic data. The other challenge comes from its unique and atypical hydration shell, stemming from the negative excess charge on its hydrogen atoms, which complicates the accurate modeling in classical atomistic simulations. In this study, we combine experimental and computer simulation techniques to devise a classical force field for NaBH4 and deepen our understanding of its characteristics. We report the first measurement of the ion's activity coefficient and extrapolate it to neutral pH conditions. Given the difficulties in directly measuring its solvation free energies, owing to its instability, we resort to quantum chemistry calculations. This combined strategy allows us to derive a set of nonpolarizable force-field parameters for the borohydride ion for classical molecular dynamics simulations. The derived force field simultaneously captures the solvation free energy, the hydration structure, as well as the activity coefficient of NaBH4 salt across a broad concentration range. The obtained insights into the hydration shell of the BH4- ion are crucial for accurately modeling and understanding its interactions with other molecules, ions, materials, and interfaces.

The formation mechanism of Sc-based metallofullerenes: a molecular dynamics simulation study.

The practical applications of endohedral metallofullerenes (EMFs) are mainly constrained by their low yields. Understanding the formation mechanisms is therefore crucial for developing methods for high-yield and selective synthesis. To address this, a novel force-field parameter set, "CSc.ff", was created using a single-parameter search optimization method, then molecular dynamics simulations of various systems with a carbon-to-scandium atomic ratio of 12.5 were carried out. The simulations were run under a constant atomic number, volume, and energy (NVE) ensemble. The influence of the working gas, helium, as well as temperature gradients on the formation process was examined. Our simulations reveal that the cage growth patterns of Sc-based EMFs (Sc-EMFs) closely resemble those of hollow fullerenes, evolving from free carbon atoms to chains, rings, and, ultimately, to cage-shaped clusters. Importantly, the Sc-EMFs formed in the simulation frequently exhibit structural defects or under-coordinated carbon atoms. Scandium atoms, whether at the periphery or on the surface of these cages, can be incorporated into the cages, forming Sc-EMFs. Helium was found to not only promote the formation of carbon cages but also facilitate the encapsulation of scandium atoms, playing a crucial role in the formation of cluster fullerenes. Moreover, cooling effectively inhibits the uncontrollable growth of the carbon cage and is essential for forming stable, appropriate-sized cages. This study enhances our understanding of the formation of Sc-EMFs and provides valuable insights for developing more efficient synthetic methods.

AutoParams: An Automated Web-Based Tool To Generate Force Field Parameters for Molecular Dynamics Simulations.

Many research questions benefit from molecular dynamics simulations to observe the motions and conformations of molecules over time, which rely on force fields that describe sets of common molecules by category. With the increase of importance for large data sets used in machine learning and growing computational efficiency, the ability to rapidly create large numbers of force field inputs is of high importance. Unusual molecules, such as nucleotide analogues, functionalized carbohydrates, and modified amino acids, are difficult to describe consistently using standard force fields, requiring the development of custom parameters for each unique molecule. While these parameters may be created by individual users, the process can become time-consuming or may introduce errors that may not be immediately apparent. We present an open-source automated parameter generation service, AutoParams, which requires minimal input from the user and creates useful Amber force field parameter sets for most molecules, particularly those that combine molecular types (e.g., a carbohydrate functionalized with a benzene). We include hierarchical atom-typing logic that makes it straightforward to expand with additional force fields and settings, and options for creating monomers in polymers, such as functionalized amino acids. It can be straightforwardly linked to any charge generation program and currently has interfaces to Psi4, PsiRESP, and TeraChem. It is open source and is available via GitHub. It includes error checking and testing protocols to ensure the parameters will be sufficient for subsequent molecular dynamics simulations and streamlines the creation of force field databases.

Machine Learning-Guided Adaptive Parametrization for Coupling Terms in a Mixed United-Atom/Coarse-Grained Model for Diphenylalanine Self-Assembly in Aqueous Ionic Liquids.

Precise regulation of the peptide self-assembly into ordered nanostructures with intriguing properties has attracted intense attention. However, predicting peptide assembly at atomic resolution is a challenge due to both the structural flexibility of peptides and the associated huge computational costs. A machine learning-guided adaptive parametrization method was proposed for developing a mixed atomic and coarse-grained (CG) model through a multiobjective optimization strategy. Our model incorporates the united-atom (UA) model for diphenylalanine (P) and the polarizable electrostatic-variable coarse-grained (VaCG) model for aqueous ionic liquid [BMIM]+[BF4]- solution. In this mixed model, the coupling van der Waals (vdW) interaction is addressed by introducing virtual sites (VS) in the UA model to interact with solvent CG beads. The coupling parameters, including the electrostatic parameter and vdW parameters, are automatically optimized through ML-guided adaptive parametrization. The performance of this model was tested by some microstructural properties, e.g., the average number of P-P intermolecular hydrogen bonds (HBs) and radius distribution functions (RDFs) between P and different fragments of IL, in comparison with all-atom (AA) simulations. The computational cost is significantly reduced using such a parametrization scheme, which could search tens of thousands of force-field parameter sets, while needing only a small fraction of them to be assessed with molecular dynamics (MD) simulations. We used such a mixed resolution model to investigate the self-assembly in IL-water mixtures with variants of IL concentration (X). The long-range-ordered fibril structure is formed in a pure water system (X = 0). With an increase of IL concentrations, the formation of an ordered self-assembly nanostructure is prohibited, instead forming branched fibril at X = 2 mol % or amorphous aggregates when X > 10 mol %, resulting from the interplay between π-stacking and HB interactions between P and IL. The qualitative agreement between the simulated structures and the observed morphologies in experiments indicates the applicability of ML-guided parametrization strategy in the study of complex systems, such as polymers, lipid bilayers, and polysaccharides.

Development of the reactive force field and silicon dry/wet oxidation process modeling

We developed the Si/O/H reactive force field parameter set and applied to silicon dry/wet oxidation process to understand the underlying physics of the thermal oxidation of the Si(100) surface. Through a systematic development of the Si/O parameter using the experimental data of the volumes of the SiO2 crystal as a reference, we reproduced the same result over the temperature range of 300–1300 K. With strategic tunings of parameters related to Si/O/H system, we carried out the simulation comparing the wet-and dry processes. A significant acceleration of the oxide film growth seen in the ‘in-situ-steam-generation (ISSG)’ is successfully represented. These properties of our model imply its applicability in wider scope. We compare our results with those obtained by using parameter set recently developed by other authors. Investigating the configuration of atoms near the interface of the SiO2 film, our model is found to be able to study the role of hydrogen atoms for the ISSG acceleration.

Assessing the Current State of Amber Force Field Modifications for DNA─2023 Edition.

Advances in molecular dynamics (MD) software alongside enhanced computational power and hardware have allowed for MD simulations to significantly expand our knowledge of biomolecular structure, dynamics, and interactions. Furthermore, it has allowed for the extension of conformational sampling times from nanoseconds to the microsecond level and beyond. This has not only made convergence of conformational ensembles through comprehensive sampling possible but consequently exposed deficiencies and allowed the community to overcome limitations in the available force fields. The reproducibility and accuracy of the force fields are imperative in order to produce biologically relevant data. The Amber nucleic acid force fields have been used widely since the mid-1980s, and improvement of these force fields has been a community effort with several artifacts revealed, corrected, and reevaluated by various research groups. Here, we focus on the Amber force fields for use with double-stranded DNA and present the assessment of two recently developed force field parameter sets (OL21 and Tumuc1). Extensive MD simulations were performed with six test systems and two different water models. We observe the improvement of OL21 and Tumuc1 compared to previous generations of the Amber DNA force. We did not detect any significant improvement in the performance of Tumuc1 compared to OL21 despite the reparameterization of bonded force field terms in the former; however, we did note discrepancies in Tumuc1 when modeling Z-DNA sequences.

Coarse-grained RNA simulations with concerted c2'/c3'-endo sugar pucker transitions.

All-atom molecular dynamic simulations are generally restricted by the time scale that can be sampled. To address this issue, various coarse-grained RNA models have been developed. These models often assume the ribose, a 5 membered ring, is fixed in only one conformation, typically the 3’-endo found in A-form helices. The models neglect the 2’-endo conformation that can be found in B-form helices and other RNA structures and is known to influence ion binding and catalytic function in addition to secondary structure propensity. Accordingly, we have developed a coarse-grained RNA force field to include transitions between the two sugar pucker states in order to accurately reproduce RNA structures and thermodynamics. To model the sugar pucker, we train a coarse-grained collective variable that distinguishes between the two states. Using this collective variable, we interpolate between two sets of force field parameters to properly reflect the structural differences between the two conformational states. The parameters of the entire model are optimized against RNA structures utilizing our contrastive divergence algorithm. Our coarse-grained RNA model opens up the opportunities to study complex RNA dynamics especially those coupled to sugar pucker transitions.

Oxidation and hydrogenation of monolayer MoS2 with compositing agent under environmental exposure: The ReaxFF Mo/Ti/Au/O/S/H force field development and applications

An atomistic modeling tool is essential to an in-depth understanding upon surface reactions of transition metal dichalcogenides (TMDs), such as molybdenum disulfide (MoS2), with the presence of compositing agents, including Ti and Au, under different environmental exposures. We report a new ReaxFF reactive force field parameter set for Mo, Ti, Au, O, S, and H interactions. We apply the force field in a series of molecular dynamics (MD) simulations to unravel the impact of the Ti dopant on the oxidation/hydrogenation behaviors of MoS2 surface. The simulation results reveal that, in the absence of Ti clusters, the MoS2 surface is ruptured and oxidized at elevated temperatures through a process of adsorption followed by dissociation of the O2 molecules on the MoS2 surface during the temperature ramp. When the MoS2 surface is exposed to H2O molecules, surface hydrogenation is most favored, followed by oxidation, then hydroxylation. The introduction of Ti clusters to the systems mitigates the oxidation/hydrogenation of MoS2 at a low or intermediate temperature by capturing the O2/H2O molecules and locking the O/H-related radicals inside the clusters. However, OH− and H3O+ are emitted from the Ti clusters in the H2O environment as temperature rises, and the accelerating hydrogenation of MoS2 is consequently observed at an ultra-high temperature. These findings indicate an important but complex role of Ti dopants in mitigating the oxidation and hydrogenation of MoS2 under different environmental exposures. The possible mechanisms of oxidation and hydrogenation revealed by MD simulations can give an insight to the design of oxidation resistant TMDs and can be useful to the optical, electronic, magnetic, catalytic, and energy harvesting industries.

Molecular Simulations Matching Denaturation Experiments for N6-Methyladenosine.

Post-transcriptional modifications are crucial for RNA function and can affect its structure and dynamics. Force-field-based classical molecular dynamics simulations are a fundamental tool to characterize biomolecular dynamics, and their application to RNA is flourishing. Here, we show that the set of force-field parameters for N6-methyladenosine (m6A) developed for the commonly used AMBER force field does not reproduce duplex denaturation experiments and, specifically, cannot be used to describe both paired and unpaired states. Then, we use reweighting techniques to derive new parameters matching available experimental data. The resulting force field can be used to properly describe paired and unpaired m6A in both syn and anti conformation, which thus opens the way to the use of molecular simulations to investigate the effects of N6 methylations on RNA structural dynamics.

Classical Force-Field Parameters for CsPbBr3 Perovskite Nanocrystals.

Understanding the chemico-physical properties of colloidal semiconductor nanocrystals (NCs) requires exploration of the dynamic processes occurring at the NC surfaces, in particular at the ligand–NC interface. Classical molecular dynamics (MD) simulations under realistic conditions are a powerful tool to acquire this knowledge because they have good accuracy and are computationally cheap, provided that a set of force-field (FF) parameters is available. In this work, we employed a stochastic algorithm, the adaptive rate Monte Carlo method, to optimize FF parameters of cesium lead halide perovskite (CsPbBr3) NCs passivated with typical organic molecules used in the synthesis of these materials: oleates, phosphonates, sulfonates, and primary and quaternary ammonium ligands. The optimized FF parameters have been obtained against MD reference trajectories computed at the density functional theory level on small NC model systems. We validated our parameters through a comparison of a wide range of nonfitted properties to experimentally available values. With the exception of the NC–phosphonate case, the transferability of the FF model has been successfully tested on realistically sized systems (>5 nm) comprising thousands of passivating organic ligands and solvent molecules, just as those used in experiments.

New Reactive Force Field for Simulations of MoS2 Crystallization.

We present a new reactive force field (ReaxFF) parameter set for simulations of Mo–S structures. We compare our parameterization to the state-of-the-art ones in their performance against density functional theory (DFT) benchmarks and MoS2 crystallization simulations. Our new force field matches DFT data significantly better than any previously published force fields and provides a realistic layered MoS2 structure in crystallization simulations. It significantly improves the state-of-the-art force fields, which tend to crystallize in the experimentally unknown rock-salt MoS structure. Therefore, our new force field is a good candidate for further development and inclusion of other practically relevant elements, such as O, C, N, and H, which can be used to study the formation and tribological or catalytical properties of molybdenum disulfide.

Open Force Field Evaluator: An Automated, Efficient, and Scalable Framework for the Estimation of Physical Properties from Molecular Simulation.

Developing accurate classical force field representations of molecules is key to realizing the full potential of molecular simulations, both as a powerful route to gaining fundamental insights into a broad spectrum of chemical and biological phenomena and for predicting physicochemical and mechanical properties of substances. The Open Force Field Consortium is an industry-funded open science effort to this end, developing open-source tools for rapidly generating new high-quality small-molecule force fields. An integral aspect of this is the parameterization and assessment of force fields against high-quality, condensed-phase physical property data, curated from open data sources such as the NIST ThermoML Archive, alongside quantum chemical data. The quantity of such experimental data in open data archives alone would require an onerous amount of human and computational resources to both curate and estimate manually, especially when estimations must be obtained for numerous sets of force field parameters. Here, we present an entirely automated, highly scalable framework for evaluating physical properties and their gradients in terms of force field parameters. It is written as a modular and extensible Python framework, which employs an intelligent multiscale estimation approach that allows for the automated estimation of properties from simulation and cached simulation data, and a pluggable API for estimation of new properties. In this study, we demonstrate the utility of the framework by benchmarking the OpenFF 1.0.0 small-molecule force field and GAFF 1.8 and GAFF 2.1 force fields against a test set of binary density and enthalpy of mixing measurements curated using the framework utilities. Further, we demonstrate the framework's utility as part of force field optimization by using it alongside ForceBalance, a framework for systematic force field optimization, to retrain a set of nonbonded van der Waals parameters against a training set of density and enthalpy of vaporization measurements.

Systematic Evaluation of Ion Diffusion and Water Exchange.

As a fundamental property of all fluids, diffusion plays myriad roles in both science and our daily lives. Diffusive properties of many liquids including water have been extensively studied both experimentally and theoretically, while for transition metal ions, there exist significant experimental data that have not been extensively studied theoretically. Hence, high-confidence predictions for challenging systems like radioactive ions that are biohazardous cannot be reliably generated. In this work, a workflow named ISAIAH (Ion Simulation using AMBER for dIffusion Action when Hydrated) was designed to accurately simulate the diffusion coefficients of 15 monoatomic ions with charges varying from -1 to +3 in four water models. As the results indicate, good agreement with experimental values was achieved, leading us to select 239Pu4+ (for which no experimental data are available) as a candidate ion to make a theoretical prediction of its diffusion coefficient in water. Among all the force field parameter sets, the ones parametrized using an augmented 12-6-4 Lennard-Jones (LJ) potential showed lower average unsigned errors (AUE) for ions of various radii and electron configurations relative to some 12-6 LJ parameters. This observation agrees well with the fact that diffusion is affected by both the hydration free energy (HFE) and the ion-oxygen distance (IOD) between solute and solvent molecules, both of which are handled well by the 12-6-4 model.

ReaxFF Force Field Development and Application for Toluene Adsorption on MnMOx (M = Cu, Fe, Ni) Catalysts.

In numerous studies, the application of the molecular dynamics scheme based on the reactive force field (ReaxFF) method has been proven effective in modeling the catalytic behavior of metal–organic compounds. Recently, this method has been successfully applied for MxOy (M = Cu, Fe, Mn, Ni) transition-metal oxides. Yet, bimetallic metal oxides of the type MnMOx (M = Cu, Fe, Ni) were also present in the experimental system but could not be modeled since not all of the force field parameters were available at the time. To bridge this gap, the force field for modeling bimetallic metal oxides had to be developed. Here, we establish the needed force field parameter sets (namely, Cu/Mn/O, Fe/Mn/O, and Ni/Mn/O) and apply them to the problem of toluene adsorption on bimetallic oxide catalyst surfaces to verify their validity. Each training set consisted of at least 10 crystal structures containing at least Cu–Mn–O, Fe–Mn–O, or Ni–Mn–O atoms in contact obtained from the available structure databases. The parameter training has been done using the in-home-compiled version of the ReaxFF code. After training the force fields for geometry reproduction, the parameters were refined using the optimization by atom charges, comparing the ReaxFF values to those obtained for the respective structures using periodic crystal density functional theory (DFT) codes. The as-developed force fields were then applied to the process of toluene adsorption/degradation on MnMOx catalysts. Results obtained show agreement with previous experimental expectations, although some remarks are given since the initially presumed crystal structure of bimetallic oxide Mn1–xMxOy crystallites may still have an impact on theoretical predictions. The presented are, to the best of the authors’ knowledge, the first applications of the ReaxFF approach to the Mn–(Cu|Fe|Ni)–O–C–H interaction.

Using Computationally-Determined Properties for Machine Learning Prediction of Self-Diffusion Coefficients in Pure Liquids.

The ability to predict transport properties of liquids quickly and accurately will greatly improve our understanding of fluid properties both in bulk and complex mixtures, as well as in confined environments. Such information could then be used in the design of materials and processes for applications ranging from energy production and storage to manufacturing processes. As a first step, we consider the use of machine learning (ML) methods to predict the diffusion properties of pure liquids. Recent results have shown that Artificial Neural Networks (ANNs) can effectively predict the diffusion of pure compounds based on the use of experimental properties as the model inputs. In the current study, a similar ANN approach is applied to modeling diffusion of pure liquids using fluid properties obtained exclusively from molecular simulations. A diverse set of 102 pure liquids is considered, ranging from small polar molecules (e.g., water) to large nonpolar molecules (e.g., octane). Self-diffusion coefficients were obtained from classical molecular dynamics (MD) simulations. Since nearly all the molecules are organic compounds, a general set of force field parameters for organic molecules was used. The MD methods are validated by comparing physical and thermodynamic properties with experiment. Computational input features for the ANN include physical properties obtained from the MD simulations as well as molecular properties from quantum calculations of individual molecules. Fluid properties describing the local liquid structure were obtained from center of mass radial distribution functions (COM-RDFs). Feature sensitivity analysis revealed that isothermal compressibility, heat of vaporization, and the thermal expansion coefficient were the most impactful properties used as input for the ANN model to predict the MD simulated self-diffusion coefficients. The MD-based ANN successfully predicts the MD self-diffusion coefficients with only a subset (2 to 3) of the available computationally determined input features required. A separate ANN model was developed using literature experimental self-diffusion coefficients as model targets. Although this second ML model was not as successful due to a limited number of data points, a good correlation is still observed between experimental and ML predicted self-diffusion coefficients.

Secondary structure dependence on simulation techniques and force field parameters: from disordered to ordered proteins.

Computer simulations are used for identifying the secondary structure properties of ordered and disordered proteins. However, our recent studies showed that the chosen computer simulation protocol, simulation technique, and force field parameter set for a disordered protein impact its predicted secondary structure properties. Here, we compare the outcome from computer simulations utilizing molecular dynamics simulations without parallel tempering techniques using various force field parameter sets and temperature-replica exchange molecular dynamics simulations both for a model ordered and two model disordered proteins. Specifically, the model ordered protein is the third IgG-binding domain of Protein G (GB3) and the two model disordered proteins are amyloid-β(1-40) and α-synuclein in water. Our findings clearly indicate that temperature-replica exchange molecular dynamics simulations and molecular dynamics simulations without special sampling techniques yield similar results for the ordered GB3 protein whereas such agreement between simulation techniques using various force field parameter sets could not be obtained for disordered proteins. These findings clearly indicate that a consensus has to be reached via further development in computer simulation technique and force field parameter sets for disordered proteins.

Machine Learning Directed Optimization of Classical Molecular Modeling Force Fields.

Accurate force fields are necessary for predictive molecular simulations. However, developing force fields that accurately reproduce experimental properties is challenging. Here, we present a machine learning directed, multiobjective optimization workflow for force field parametrization that evaluates millions of prospective force field parameter sets while requiring only a small fraction of them to be tested with molecular simulations. We demonstrate the generality of the approach and identify multiple low-error parameter sets for two distinct test cases: simulations of hydrofluorocarbon (HFC) vapor-liquid equilibrium (VLE) and an ammonium perchlorate (AP) crystal phase. We discuss the challenges and implications of our force field optimization workflow.

CHARMM Force-Field Parameters for Morphine, Heroin, and Oliceridine, and Conformational Dynamics of Opioid Drugs.

Opioid drug binding to specialized G protein-coupled receptors (GPCRs) can lead to analgesia upon activation via downstream Gi protein signaling and to severe side effects via activation of the β-arrestin signaling pathway. Knowledge of how different opioid drugs interact with receptors is essential, as it can inform and guide the design of safer therapeutics. We performed quantum and classical mechanical computations to explore the potential energy landscape of four opioid drugs: morphine and its derivatives heroin and fentanyl and for the unrelated oliceridine. From potential energy profiles for bond twists and from interactions between opioids and water, we derived a set of force-field parameters that allow a good description of structural properties and intermolecular interactions of the opioids. Potential of mean force profiles computed from molecular dynamics simulations indicate that fentanyl and oliceridine have complex energy landscapes with relatively small energy penalties, suggesting that interactions with the receptor could select different binding poses of the drugs.

Comparison of different approaches to derive classical bonded force-field parameters for a transition metal cofactor: a case study for non-heme iron site of ectoine synthase

Computational investigations into the structure and function of metalloenzymes with transition metal cofactors require proper preparation of the model, which requires obtaining reliable force field parameters for the cofactor. Here, we present a test case where several methods were used to derive amber force field parameters for a bonded model of the Fe(II) cofactor of ectoine synthase. Moreover, the spin of the ground state of the cofactor was probed by DFT and post-HF methods, which consistently indicated the quintet state is lowest in energy and well separated from triplet and singlet. The performance of the obtained force field parameter sets, derived for the quintet spin state, was scrutinized and compared taking into account metrics focused on geometric features of the models as well as their energetics. The main conclusion of this study is that Hessian-based methods yield parameters which represent the geometry around the metal ion, but poorly reproduce energy variance with geometrical changes. On the other hand, the energy-based method yields parameters accurately reproducing energy-structure relationships, but with bad performance in geometry optimization. Preliminary tests show that admixing geometrical criteria to energy-based methods may allow to derive parameters with acceptable performance for both energy and geometry.

Secondary structure dependence of amyloid-β(1-40) on simulation techniques and force field parameters.

Our recent studies revealed that none of the selected widely used force field parameters and molecular dynamics simulation techniques yield structural properties for the intrinsically disordered α-synuclein that are in agreement with various experiments via testing different force field parameters. Here, we extend our studies on the secondary structure properties of the disordered amyloid-β(1-40) peptide in aqueous solution. For these purposes, we conducted extensive replica exchange molecular dynamics simulations and obtained extensive molecular dynamics simulation trajectories from David E. Shaw group. Specifically, these molecular dynamics simulations were conducted using various force field parameters and obtained results are compared to our replica exchange molecular dynamics simulations and experiments. In this study, we calculated the secondary structure abundances and radius of gyration values for amyloid-β(1-40) that were simulated using varying force field parameter sets and different simulation techniques. In addition, the intrinsic disorder propensity, as well as sequence-based secondary structure predisposition of amyloid-β(1-40) and compared the findings with the results obtained from molecular simulations using various force field parameters and different simulation techniques. Our studies clearly show that the epitope region identification of amyloid-β(1-40) depends on the chosen simulation technique and chosen force field parameters. Based on comparison with experiments, we find that best computational results in agreement with experiments are obtained using the a99sb*-ildn, charmm36m, and a99sb-disp parameters for the amyloid-β(1-40) peptide in molecular dynamics simulations without parallel tempering or via replica exchange molecular dynamics simulations.