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The phonon-related properties of crystalline polymers are highly relevant for various applications. Their simulation is, however, particularly challenging, as the systems that need to be modeled are often too extended to be treated by ab initio methods, while classical force fields are too inaccurate. Machine-learned potentials parametrized against material-specific ab initio data hold the promise of being extremely accurate and also highly efficient. Still, for their successful application, protocols for their parametrization need to be established to ensure an optimal performance, and the resulting potentials need to be thoroughly benchmarked. These tasks are tackled in the current manuscript, where we devise a protocol for parametrizing moment tensor potentials (MTPs) to describe the structural properties, phonon band structures, elastic constants, and forces in molecular dynamics simulations for three prototypical crystalline polymers: polyethylene (PE), polythiophene (PT), and poly-3-hexylthiophene (P3HT). For PE, the thermal conductivity and thermal expansion are also simulated and compared to experiments. A central element of the approach is to choose training data in view of the considered use case of the MTPs. This not only yields a massive speedup for complex calculations while essentially maintaining DFT accuracy, but also enables the reliable simulation of properties that, so far, have been entirely out of reach. [ABSTRACT FROM AUTHOR]

In the field of molecular simulation for drug design, traditional molecular mechanic force fields and quantum chemical theories have been instrumental but limited in terms of scalability and computational efficiency. To overcome these limitations, machine learning force fields (MLFFs) have emerged as a powerful tool capable of balancing accuracy with efficiency. MLFFs rely on the relationship between molecular structures and potential energy, bypassing the need for a preconceived notion of interaction representations. Their accuracy depends on the machine learning models used, and the quality and volume of training data sets. With recent advances in equivariant neural networks and high‐quality datasets, MLFFs have significantly improved their performance. This review explores MLFFs, emphasizing their potential in drug design. It elucidates MLFF principles, provides development and validation guidelines, and highlights successful MLFF implementations. It also addresses potential challenges in developing and applying MLFFs. The review concludes by illuminating the path ahead for MLFFs, outlining the challenges to be overcome and the opportunities to be harnessed. This inspires researchers to embrace MLFFs in their investigations as a new tool to perform molecular simulations in drug design. [ABSTRACT FROM AUTHOR]

The presence of antibiotics in soils is increasing drastically in last decades due to the intensive farming industry and excessive human consumption. Clay minerals are one of the soil components with great adsorption capacity for organic pollutants. The study of interactions between antibiotics and mineral surfaces will give us scientific knowledge of these pollutants through soils. In this work, we study the adsorption of the antibiotic ciprofloxacin in the clay mineral fraction of soils from the Argentinian zone of Santa Rosa (Corrientes), in a collaborative research of experiments and atomistic modelling calculations of the intercalation of ciprofloxacin in the interlayer space of montmorillonite. Adsorption and desorption isotherms were performed and compared with different isotherm models. Additionally, enthalpy, entropy, and free energy were determined from equilibrium constants at a function of temperature. All these experiments and calculations lead to the conclusions that two adsorption types of ciprofloxacin are found on clay minerals: one weakly sorbed that is released during the desorption experiments, and other one strongly joined that remains in the soil. [ABSTRACT FROM AUTHOR]

Damage induced by transient disruption and mechanoporation in an intact cell membrane is a vital nanoscale biomechanical mechanism that critically affects cell viability. To complement experimental studies of mechanical membrane damage and disruption, molecular dynamics (MD) simulations have been performed at different force field resolutions, each of which follows different parameterization strategies and thus may influence the properties and dynamics of membrane systems. Therefore, the current study performed tensile deformation MD simulations of bilayer membranes using all-atom (AA), united-atom (UA), and coarse-grained Martini (CG-M) models to investigate how the damage biomechanics differs across atomistic and coarse-grained (CG) simulations. The mechanical response and mechanoporation damage were qualitatively similar but quantitatively different in the three models, including some progressive changes based on the coarse-graining level. The membranes yielded and reached ultimate strength at similar strains; however, the coarser systems exhibited lower average yield stresses and failure strains. The average failure strain in the UA model was approximately 7% lower than the AA, and the CG-M was 20% lower than UA and 27% lower than AA. The CG systems also nucleated a higher number of pores and larger pores, which resulted in higher damage during the deformation process. Overall, the study provides insight on the impact of force field—a critical factor in modeling biomolecular systems and their interactions—in inspecting membrane mechanosensitive responses and serves as a reference for justifying the appropriate force field for future studies of more complex membranes and more diverse biomolecular assemblies. [ABSTRACT FROM AUTHOR]

The heat transfer and oxygen transport behaviors of Ti-6Al and Fe-12Cr alloys directly affect their aeronautical material application. In order to investigate the flame retardant mechanism in atomic scale, molecular dynamic (MD) simulations were performed to study the heat transfer and oxygen transport performances of Ti-6Al and Fe-12Cr alloys in the linear and uniform heating treatment. Next, the ignition performances of Ti-6Al and Fe-12Cr alloys were further verified by the frictional ignition tests. Therefore, this is a multi-scale research with a novel combination of MD simulations and ignition tests. Those obtained results show that the heat is more concentrated at the heating edge of Ti-6Al alloy while at the same heating rate. This leads to the intensification of atomic motion at the heating edge, and reduces the structural thermal stability of Ti-6Al alloy's heating edge. Therefore, the heat transfer performance of Fe-12Cr alloy at the same state is better than that of Ti-6Al alloy. When there is an oxide layer on the surface of these two alloys, Fe-12Cr alloy can inhibit the oxygen transfer more effectively than Ti-6Al alloy, and further improve its flame retardant property. In addition, the frictional ignition tests with Ti alloy rotor under the same airflow environment show that the critical airflow velocity of the Fe-12Cr alloy stator is about 114 m/s higher than that of Ti-6Al-4V alloy stator at 384∘. Generally, according to MD simulations and frictional ignition tests, it could be concluded that the flame retardancy of Fe-12Cr alloy is higher than Ti-6Al alloy. [ABSTRACT FROM AUTHOR]

Machine learning potentials, particularly the deep potential (DP) model, have revolutionized molecular dynamics (MD) simulations, striking a balance between accuracy and computational efficiency. To facilitate the DP model's integration with the popular MD engine OpenMM, we have developed a versatile OpenMM plugin. This plugin supports a range of applications, from conventional MD simulations to alchemical free energy calculations and hybrid DP/MM simulations. Our extensive validation tests encompassed energy conservation in microcanonical ensemble simulations, fidelity in canonical ensemble generation, and the evaluation of the structural, transport, and thermodynamic properties of bulk water. The introduction of this plugin is expected to significantly expand the application scope of DP models within the MD simulation community, representing a major advancement in the field. [ABSTRACT FROM AUTHOR]

Context: Ryanodine receptors (RyRs) are large intracellular ligand-gated calcium release ion channels. Mutations in human RyR1 in combination with a volatile anesthetic or muscle relaxant are known to cause leaky RyRs resulting in malignant hyperthermia (MH). This has long been primarily treated with the RyR inhibitory drug dantrolene. Alternatives to dantrolene as a RyR inhibitor may be found through computer-aided drug design. Additionally, molecular dynamics (MD) studies of dantrolene interacting with RyRs may reveal its full mechanism of action. The availability of accurate force field parameters is important for the success of both. Methods: In this study, force field parameters for dantrolene were obtained from the CHARMM General Force Field (CGenFF) program and optimized using the force field toolkit (FFTK) and FFParam programs. The obtained parameters were then validated by a comparison between calculated and experimental IR spectra and normal mode analysis, among other techniques. [ABSTRACT FROM AUTHOR]

Based on the TraPPE force field, previously unknown values of the parameters of the intermolecular interaction potential of trans-decalin were determined. Parametrization was carried out using experimental data on saturated vapor pressure and density at atmospheric pressure. The found parameters make it possible to adequately describe the boiling and condensation lines of trans-decalin and also predict the critical values of pressure, density, and temperature with satisfactory accuracy. Calculations of vapor-liquid phase equilibrium conditions for a binary CO2—trans-decalin mixture in supercritical conditions for CO2 were carried out. When quantitatively comparing the calculated values with experimental data, an underestimation of pressure at a temperature of 345.4 K by 30% is observed, which decreases to 5% for temperatures up to 525 K. [ABSTRACT FROM AUTHOR]

The standard GAFF2 force field parameterization has been refined for the fluorinated alcohols 2,2,2‐trifluoroethanol (TFE), 1,1,1,3,3,3‐hexafluoro‐2‐propanol (HFIP), and 1,1,1,3,3,3‐hexafluoropropan‐2‐one (HFA), which are commonly used to study proteins and peptides in biomimetic media. The structural and dynamic properties of both proteins and peptides are significantly influenced by the biomimetic environment created by the presence of these cosolvents in aqueous solutions. Quantum mechanical calculations on stable conformers were used to parameterize the atomic charges. Different systems, such as pure liquids, aqueous solutions, and systems formed by melittin protein and cosolvent/water solutions, have been used to validate the new models. The calculated macroscopic and structural properties are in agreement with experimental findings, supporting the validity of the newly proposed models. [ABSTRACT FROM AUTHOR]

The present study investigates the interaction between two alkylating agent drugs, cyclophosphamide (CP) and mechlorethamine (MC), with Single-Wall carbon nanotubes (SWCNTs) and Boron Nitride nanotubes (BNNTs). Calculations have been performed by using methods of quantum mechanics and molecular mechanics. The effects of different solvents on the interaction of CP and MC with SWCNTs and BNNTs within the Onsager self-consistent reaction field (SCRF) model, as well as the effects of temperature on the stability of the interactions between the compounds in various solvents have been investigated. Thermodynamic parameters, Frontier Molecular Orbitals (FMOs), and Total Density of States (DOS) of the title compounds have been evaluated by using theoretical calculations. Moreover, the interaction of CP and MC with SWCNTs and BNNTs have been examined through AMBER, OPLS, CHARMM, and MM+ force fields through the molecular mechanic (MM) method. The Monte Carlo simulation of CP and MC structures connected to carbon and boron nitride nanotubes in Water, Methanol, Ethanol, DMSO, and Chloroform solvents showed that Chloroform is the most stable solvent for simulation with the lowest energy, which is directly attributed to dielectric constant. Studies show that the results of Monte Carlo, molecular mechanics, and quantum mechanics are consistent with each other in terms of thermodynamic properties and conformer populations. [ABSTRACT FROM AUTHOR]

Electron paramagnetic resonance (EPR) has become a powerful probe of conformational heterogeneity and dynamics of biomolecules. In this Review, we discuss different computational modeling techniques that enrich the interpretation of EPR measurements of dynamics or distance restraints. A variety of spin labels are surveyed to provide a background for the discussion of modeling tools. Molecular dynamics (MD) simulations of models containing spin labels provide dynamical properties of biomolecules and their labels. These simulations can be used to predict EPR spectra, sample stable conformations and sample rotameric preferences of label sidechains. For molecular motions longer than milliseconds, enhanced sampling strategies and de novo prediction software incorporating or validated by EPR measurements are able to efficiently refine or predict protein conformations, respectively. To sample large‐amplitude conformational transition, a coarse‐grained or an atomistic weighted ensemble (WE) strategy can be guided with EPR insights. Looking forward, we anticipate an integrative strategy for efficient sampling of alternate conformations by de novo predictions, followed by validations by systematic EPR measurements and MD simulations. Continuous pathways between alternate states can be further sampled by WE‐MD including all intermediate states. [ABSTRACT FROM AUTHOR]

The amyloid‐forming Aβ peptide is able to interact with metal cations to form very stable complexes that influence fibril formation and contribute to the onset of Alzheimer's disease. Multiple structures of peptides derived from Aβ in complex with different metals have been resolved experimentally to provide an atomic‐level description of the metal‐protein interactions. However, Aβ is intrinsically disordered, and hence more amenable to an ensemble description. Molecular dynamics simulations can now reach the timescales needed to generate ensembles for these type of complexes. However, this requires accurate force fields both for the protein and the protein‐metal interactions. Here we use state‐of‐the‐art methods to generate force field parameters for the Zn(II) cations in a set of complexes formed by different Aβ variants and combine them with the Amber99SB*‐ILDN optimized force field. Upon comparison of NMR experiments with the simulation results, further optimized with a Bayesian/Maximum entropy approach, we provide an accurate description of the molecular ensembles for most Aβ‐metal complexes. We find that the resulting conformational ensembles are more heterogeneous than the NMR models deposited in the Protein Data Bank. [ABSTRACT FROM AUTHOR]

This paper presents a United Atom (UA) force field for simulating hydrocarbon molecules in bituminous materials, integrating explicit hydrogens into beads with their parent atom. This method simplifies all-atom molecular models, significantly accelerating Molecular Dynamics (MD) simulations of bitumen by 10 to 100 times. Key advantages include halving the particle count, eliminating complex hydrogen interactions, and decreasing the degrees of freedom of the molecules. Developed by mapping forces from an all-atom model to the centers of mass of UA model beads, the force field ensures accurate replication of energies, forces, and molecular conformations, mirroring properties like pressure and density. It features 17 bead types and 287 interaction types, encompassing various hydrocarbon molecules. The UA force field's stability, surpassing all-atom models, is a notable achievement. This stability, stemming from smoother potential energy surfaces, leads to consistent property measurements and improved stress tensor accuracy. It enables the extension of MD simulations to larger spatiotemporal scales, crucial for understanding complex phenomena such as phase separation in bituminous materials. This foundational work sets the stage for future developments, including refining parameters and introducing new bead types, to enhance the modeling capabilities of the force field, thereby advancing the application and understanding of bituminous materials.

In this paper, my purpose is to take into serious consideration Adorno's explicit conception of the artwork as a force field. With this expression he intends to emphasize the inner constitution of the artwork as a movement of antagonistic tensions, a dynamic of elements that are not simply juxtaposed, but dialectically interacting with one another. In a similar configuration, the aesthetic experience of the artwork consists in letting their friction explode to its extreme, achieving a balance which remains nevertheless substantially precarious and inconclusive, ready to be immediately set in motion again. Thanks to the aesthetic trait of the force field, these tensions are brought to unity in a way that it does not suppress the enactment of antagonisms, but keeps their multiplicity alive: Homer's tale of Penelope as an allegory of art. [ABSTRACT FROM AUTHOR]

Characterizing structural ensembles of intrinsically disordered proteins (IDPs) and intrinsically disordered regions (IDRs) of proteins is essential for studying structure–function relationships. Due to the different neutron scattering lengths of hydrogen and deuterium, selective labeling and contrast matching in small‐angle neutron scattering (SANS) becomes an effective tool to study dynamic structures of disordered systems. However, experimental timescales typically capture measurements averaged over multiple conformations, leaving complex SANS data for disentanglement. We hereby demonstrate an integrated method to elucidate the structural ensemble of a complex formed by two IDRs. We use data from both full contrast and contrast matching with residue‐specific deuterium labeling SANS experiments, microsecond all‐atom molecular dynamics (MD) simulations with four molecular mechanics force fields, and an autoencoder‐based deep learning (DL) algorithm. From our combined approach, we show that selective deuteration provides additional information that helps characterize structural ensembles. We find that among the four force fields, a99SB‐disp and CHARMM36m show the strongest agreement with SANS and NMR experiments. In addition, our DL algorithm not only complements conventional structural analysis methods but also successfully differentiates NMR and MD structures which are indistinguishable on the free energy surface. Lastly, we present an ensemble that describes experimental SANS and NMR data better than MD ensembles generated by one single force field and reveal three clusters of distinct conformations. Our results demonstrate a new integrated approach for characterizing structural ensembles of IDPs. [ABSTRACT FROM AUTHOR]

In the last few decades, deep learning (DL) has afforded solutions to macroscopic problems in petroleum engineering, but mechanistic problems at the microscale have not benefited from it. Mechanism studies have been the strong demands for the emerging projects, such as the gas storage and hydrate production, and for some problems encountered in the storage process, which are common found as the chemical interaction between injected gas and mineral, and the formation of hydrate. Emerging advances in DL technology enable solving molecular dynamics (MD) with quantum accuracy. The conventional quantum chemical method is computational expensive, whereas the classical MD method cannot guarantee high accuracy because of its empirical force field parameters. With the help of the DL force field, precision at the quantum chemistry level can be achieved in MD. Moreover, the DL force field promotes the computational speed compared with first-principles calculations. In this review, the basic knowledge of the molecular force field and deep neural network (DNN) is first introduced. Then, three representative open-source packages relevant to the DL force field are introduced. As the most common components in the development of oil and gas reservoirs, water and methane are studied from the aspects of computational efficiency and chemical reaction respectively, providing the foundation of oil and gas researches. However, in the oil and gas problems, the complex molecular topo structures and various element types set a high challenge for the DL techniques in MD. Regarding the computational efficiency, it needs improvement via GPU and parallel accelerations to compete with classical MD. Even with such difficulties, the booming of this technique in the area of petroleum engineering can be predictable.

The presence of antibiotics in soils is increasing drastically in last decades due to the intensive farming industry and excessive human consumption. Clay minerals are one of the soil components with great adsorption capacity for organic pollutants. The study of interactions between antibiotics and mineral surfaces will give us scientific knowledge of these pollutants through soils. In this work, we study the adsorption of the antibiotic ciprofloxacin in the clay mineral fraction of soils from the Argentinian zone of Santa Rosa (Corrientes), in a collaborative research of experiments and atomistic modelling calculations of the intercalation of ciprofloxacin in the interlayer space of montmorillonite. Adsorption and desorption isotherms were performed and compared with different isotherm models. Additionally, enthalpy, entropy, and free energy were determined from equilibrium constants at a function of temperature. All these experiments and calculations lead to the conclusions that two adsorption types of ciprofloxacin are found on clay minerals: one weakly sorbed that is released during the desorption experiments, and other one strongly joined that remains in the soil.

With the advent of the resolution revolution in cryoelectron microscopy (cryo-EM), low-resolution refinement is common, and likewise increases the need for a reliable force field. Here, we report on the incorporation of the OPLS3e force field with the VSGB2.1 solvation model in the structure determination package Phenix. Our results show significantly improved structure quality and reduced ligand strain at lower resolution for X-ray refinement. For refinement of cryo-EM-based structures, we find comparable quality structures, goodness-of-fit, and reduced ligand strain. We also show how structure quality and ligand strain are related to the map-model cross-correlation as a function of data weight, and how that can detect overfitting. Signs of overfitting are found in over half of our cryo-EM dataset, which can be remedied by a re-refinement at a lower data weight. Finally, a start-to-end script for refining structures with Phenix/OPLS3e is available in the Schrödinger 2020-3 distribution.

Crystallization of alkaline earth metal carbonates from water is important for biomineralization and environmental geochemistry. Here, large-scale computer simulations are a useful approach to complement experimental studies by providing atomistic insights and even by quantitatively determining the thermodynamics of individual steps. However, this is dependent on the existence of force field models that are sufficiently accurate while being computationally efficient enough to sample complex systems. Here, we introduce a revised force field for aqueous alkaline earth metal carbonates that reproduces both the solubilities of the crystalline anhydrous minerals, as well as the hydration free energies of the ions. The model is also designed to run efficiently on graphical processing units thereby reducing the cost of such simulations. The performance of the revised force field is compared against previous results for important properties relevant to crystallization, including ion-pairing and mineral–water interfacial structure and dynamics. This article is part of a discussion meeting issue 'Supercomputing simulations of advanced materials'. [ABSTRACT FROM AUTHOR]

Hydrogel is a three‐dimensional cross‐linked hydrophilic network that can imbibe a large amount of water inside its structure (up to 99% of its dry weight). Due to their unique characteristics of biocompatibility and flexibility, it has found applications in diversified fields, including tissue engineering, drug delivery, biosensors, and agriculture. Even though hydrogels are widely used in the biomedical field, their lower mechanical strength still limits their application to its full potential. Hydrogels can be reinforced with organic, inorganic, and metal‐based nanofillers to improve their mechanical strength. Due to improved computational power, computational‐based techniques are emerging as a leading characterization technique for nanocomposites and hydrogels. In nanomaterials, atomistic description governs the mechanical strength and thermal behavior that realized atomistic level simulations as an appropriate approach to capture the deformation governing mechanism. Among atomistic simulations, the molecular dynamics (MD)‐based approach is emerging as a prospective technique for simulating neat and nanocomposite‐based hydrogels' mechanical and thermal behavior. The success and accuracy of MD simulation entirely depend on the force field. This review article will compile the force field employed by the research community to capture the atomistic interactions in different nanocomposite‐based hydrogels. This article will comprehensively review the progress made in the atomistic approach to study neat and nanocomposite‐based hydrogels' properties. The authors have enlightened the challenges and limitations associated with the atomistic modeling of hydrogels. This article is categorized under:Structure and Mechanism > Computational Materials ScienceMolecular and Statistical Mechanics > Molecular Dynamics and Monte‐Carlo MethodsSoftware > Molecular Modeling [ABSTRACT FROM AUTHOR]

Intrinsically disordered proteins (IDPs) are widely distributed across eukaryotic cells, playing important roles in molecular recognition, molecular assembly, post-translational modification, and other biological processes. IDPs are also associated with many diseases such as cancers, cardiovascular diseases, and neurodegenerative diseases. Due to their structural flexibility, conventional experimental methods cannot reliably capture their heterogeneous structures. Molecular dynamics simulation becomes an important complementary tool to quantify IDP structures. This review covers recent force field strategies proposed for more accurate molecular dynamics simulations of IDPs. The strategies include adjusting dihedral parameters, adding grid-based energy correction map (CMAP) parameters, refining protein-water interactions, and others. Different force fields were found to perform well on specific observables of specific IDPs but also are limited in reproducing all available experimental observables consistently for all tested IDPs. We conclude the review with perspective areas for improvements for future force fields for IDPs.

Abstract Molecular representation learning is a crucial task to accelerate drug discovery and materials design. Graph neural networks (GNNs) have emerged as a promising approach to tackle this task. However, most of them do not fully consider the intramolecular interactions, i.e. bond stretching, angle bending, torsion, and nonbonded interactions, which are critical for determining molecular property. Recently, a growing number of 3D-aware GNNs have been proposed to cope with the issue, while these models usually need large datasets and accurate spatial information. In this work, we aim to design a GNN which is less dependent on the quantity and quality of datasets. To this end, we propose a force field-inspired neural network (FFiNet), which can include all the interactions by incorporating the functional form of the potential energy of molecules. Experiments show that FFiNet achieves state-of-the-art performance on various molecular property datasets including both small molecules and large protein–ligand complexes, even on those datasets which are relatively small and without accurate spatial information. Moreover, the visualization for FFiNet indicates that it automatically learns the relationship between property and structure, which can promote an in-depth understanding of molecular structure.

Machine learning potentials, particularly the deep potential (DP) model, have revolutionized molecular dynamics (MD) simulations, striking a balance between accuracy and computational efficiency. To facilitate the DP model’s integration with the popular MD engine OpenMM, we have developed a versatile OpenMM plugin. This plugin supports a range of applications, from conventional MD simulations to alchemical free energy calculations and hybrid DP/MM simulations. Our extensive validation tests encompassed energy conservation in microcanonical ensemble simulations, fidelity in canonical ensemble generation, and the evaluation of the structural, transport, and thermodynamic properties of bulk water. The introduction of this plugin is expected to significantly expand the application scope of DP models within the MD simulation community, representing a major advancement in the field.

Low-intensity pulsed ultrasound (LIPUS) is commonly used for bone fracture healing to mechanically stimulate a fracture site and accelerate the repair process. Recent studies reported that LIPUS stimulation could have various effectiveness such as not only fracture healing but also regenerative medicine of articular cartilage and cognitive dysfunction improvement. However, conventional LIPUS devices stimulate both affected and unaffected sites. To promote healing effects at an affected site and enhance therapeutic applications, LIPUS devices should employ acoustic holography, which can control force fields and provide the desired ones to an affected site. However, conventional control algorisms cannot form desired force fields and have not been used in vivo for therapy. In this study, we experimentally investigate the feasibility to control force fields by acoustic holography using an ultrasound transducer array under water for therapy. First, we designed and prototyped a simple device for generating force fields using a 4×4 ultrasound transducer array under water for therapy. Then, we introduced a force field control algorithm developed in our previous study into the prototype force field generator to experimentally verify the controllability to achieve desired force fields for therapy. The findings show that the prototype force field generator meets acoustic intensity enough for fracture healing by focused ultrasound and that the developed control algorism applied to the prototype force field generator forms a two-dimensional force field similar to a rectangular shape assuming a fracture line as a target area and suppresses the acoustic intensity outside the target area.

Thermal conductivities of polybutadiene crosslinked with sulfur as a function of the heat flux autocorrelation function by using an equilibrium molecular dynamic (EMD) simulation were investigated. The Green–Kubo method was used to calculate thermal conductivities. All simulations were performed by applying the LAMMPS software (version 3 Mar 2020) package. The united-atom force field (OPLS-UA) from the Moltemplate software (version 2.20.3) was applied in the simulations. The influence of uniform and random distributions of sulfur in polybutadiene on the final value of thermal conductivities was studied by polymeric model structures with similar and variable degrees of crosslinking. The results showed that for identical degrees of crosslinking, the distribution of crosslinkers in the polymeric model structures significantly influenced the final value of thermal conductivity. Moreover, the influence of the crosslinking degree on the final value of thermal conductivity was studied by considering polymeric model structures with different degrees of crosslinking. The results demonstrate that by having a random distribution of sulfur, the thermal conductivity will be enhanced. However, by increasing the degree of crosslinking to the higher percentage in random crosslinked model structures, the value of thermal conductivity drops significantly due to possible higher crystallization of the model structures, which decrease the degree of freedom for phonon contributions. [ABSTRACT FROM AUTHOR]

This research focuses on obtaining the behavior of chemical compounds with respect to their molecular weight and optimization energy based on the variation in properties in organic carbon links. Here, behavioral analysis of compounds is used in the application of a metal organic framework to denote the high-grade compounds. The grade was selected based on the essential measure of optimization energy and molecular weight, and in turn, depicts the stability of material. Computation of the optimization energy and molecular weight of chemical compounds was performed with Avogadro software. Several force fields can be considered to compute optimized energy. Exclusively, three force fields, namely, the Universal Force Field (UFF), the General Amber Force Field (GAFF), and the Ghemical force field (Ghemical) were selected from Avogadro as these were more relevant to compounds considered in this research. The various chemical compounds examined in this work are Aluminum (Al), Boron (Br), Calcium (Ca), Chlorine (Cl), Indium (In), Potassium (K), Scandium (Sc), Silicon (Si), and Tungsten (W). Hence, molecular modeling of different compounds incorporated with three different force fields was evaluated in this work. In this study, we found that the In structure has more energy reduction, of 22.673 kJ mol−1 in UFF, when compared with the other two force fields. Thus, In has higher potential with more stability. [ABSTRACT FROM AUTHOR]

One of the main concerns of Anfinsen was to reveal the connection between the amino-acid sequence and their biologically active conformation. This search gave rise to two crucial questions in structural biology, namely, why the proteins fold and how a sequence encodes its folding. As to the why, he proposes a plausible answer, namely, the thermodynamic hypothesis. As to the how, this remains an unsolved challenge. Consequently, the protein folding problem is examined here from a new perspective, namely, as an 'analytic whole'. Conceiving the protein folding in this way enabled us to (i) examine in detail why the force-field-based approaches have failed, among other purposes, in their ability to predict the three-dimensional structure of a protein accurately; (ii) propose how to redefine them to prevent these shortcomings, and (iii) conjecture on the origin of the state-of-the-art numerical-methods success to predict the tridimensional structure of proteins accurately. [ABSTRACT FROM AUTHOR]

The applicability of the three steps systematic parametrization procedure (3SSPP) to develop a force field for primary amines was evaluated in the present work. Previous simulations of primary amines show that current force fields (FF) can underestimate some experimental values under room conditions. Therefore, we propose a new set of parameters, for an united atom (UA) model, that can be used for short and long amines which predict correctly thermodynamic and dynamical properties. Following the 3SSPP methodology, the partial charges are chosen to match the experimental dielectric constant whereas the Lennard-Jones (LJ) parameters, 𝜖 and 𝜎, are fitted to reproduce the surface tension at the vapor-liquid interface and the liquid density, respectively. Simulations were initially conducted for the propylamine molecule by introducing three different types of carbon atoms, C𝛼 and C𝛽, with electric charges, and C𝑛, without charge. Then, modifying the charges of the carbons and using the transferable LJ parameters, the new set of constants for long amines were found. The results show good agreement for the experimental dielectric constant and mass density with a percentage error less than 1%, whereas for the surface tension the error is up to 4%. For the short amines, methylamine and ethylamine, the new charges were obtained from a fitting function calculated from the long amines results. For these molecules, the values of the dielectric constant and the surface tension present errors of the order of 10% with the experimental data. Miscibility of the amines was also tested with the new parameters and the results show reasonable agreement with experiments. [ABSTRACT FROM AUTHOR]

Background: Force fields are used in a wide variety of contexts for classical molecular simulation, including studies on protein-ligand binding, membrane permeation, and thermophysical property prediction. The quality of these studies relies on the quality of the force fields used to represent the systems. Methods: Focusing on small molecules of fewer than 50 heavy atoms, our aim in this work is to compare nine force fields: GAFF, GAFF2, MMFF94, MMFF94S, OPLS3e, SMIRNOFF99Frosst, and the Open Force Field Parsley, versions 1.0, 1.1, and 1.2. On a dataset comprising 22,675 molecular structures of 3,271 molecules, we analyzed force field-optimized geometries and conformer energies compared to reference quantum mechanical (QM) data. Results: We show that while OPLS3e performs best, the latest Open Force Field Parsley release is approaching a comparable level of accuracy in reproducing QM geometries and energetics for this set of molecules. Meanwhile, the performance of established force fields such as MMFF94S and GAFF2 is generally somewhat worse. We also find that the series of recent Open Force Field versions provide significant increases in accuracy. Conclusions: This study provides an extensive test of the performance of different molecular mechanics force fields on a diverse molecule set, and highlights two (OPLS3e and OpenFF 1.2) that perform better than the others tested on the present comparison. Our molecule set and results are available for other researchers to use in testing.

This paper reviews molecular dynamics (MD) concepts on heat transfer analysis of supercritical CO2, and highlights the major parameters that can affect the accuracy of respective thermal coefficients. Subsequently, the prime aspects of construction, transfer identification, and thermal performance are organized according to their challenges and prospective solutions associated with the mutability of supercritical CO2 properties. Likewise, the characteristics of bound force field schemes and thermal relaxation approaches are discussed on a case-by-case basis. Both convective and diffusive states of trans- and supercritical CO2 are debated, given their magnitude effects on molecular interactions. Following the scarcity of literature on similar enquiries, this paper recommended a future series of studies on molecular dynamics models in a large region of supercriticality and phase-interactions for coupled heat and mass transfer systems. This review recognizes that the foremost undertaking is to ascertain the thermo-hydraulic identity of supercritical CO2 for process feasibility of developed technology. [ABSTRACT FROM AUTHOR]

Boron containing compounds (BCCs) aroused increasing interest in the scientific community due to their wide application as drugs in various fields. In order to design new compounds hopefully endowed with pharmacological activity and also investigate their conformational behavior, the support of computational studies is crucial. Nevertheless, the suitable molecular mechanics parameterization and the force fields needed to perform these simulations are not completely available for this class of molecules. In this paper, Amber force field parameters for phenyl-, benzyl-, benzylamino-, and methylamino-boronates, a group of boron-containing compounds involved in different branches of the medicinal chemistry, were created. The robustness of the obtained data was confirmed through molecular dynamics simulations on ligand/β-lactamases covalent complexes. The ligand torsional angles, populated over the trajectory frames, were confirmed by values found in the ligand geometries, located through optimizations at the DFT/B3LYP/6-31g(d) level, using water as a solvent. In summary, this study successfully provided a library of parameters, opening the possibility to perform molecular dynamics simulations of this class of boron-containing compounds. [ABSTRACT FROM AUTHOR]

Host–guest binding, despite the relatively simple structural and chemical features of individual components, still poses a challenge in computational modelling. The extreme underperformance of standard end-point methods in host–guest binding makes them practically useless. In the current work, we explore a potentially promising modification of the three-trajectory realization. The alteration couples the binding-induced structural reorganization into free energy estimation and suffers from dramatic fluctuations in internal energies in protein–ligand situations. Fortunately, the relatively small size of host–guest systems minimizes the magnitude of internal fluctuations and makes the three-trajectory realization practically suitable. Due to the incorporation of intra-molecular interactions in free energy estimation, a strong dependence on the force field parameters could be incurred. Thus, a term-specific investigation of transferable GAFF derivatives is presented, and noticeable differences in many aspects are identified between commonly applied GAFF and GAFF2. These force-field differences lead to different dynamic behaviors of the macrocyclic host, which ultimately would influence the end-point sampling and binding thermodynamics. Therefore, the three-trajectory end-point free energy calculations are performed with both GAFF versions. Additionally, due to the noticeable differences between host dynamics under GAFF and GAFF2, we add additional benchmarks of the single-trajectory end-point calculations. When only the ranks of binding affinities are pursued, the three-trajectory realization performs very well, comparable to and even better than the regressed PBSA_E scoring function and the dielectric constant-variable regime. With the GAFF parameter set, the TIP3P water in explicit solvent sampling and either PB or GB implicit solvent model in free energy estimation, the predictive power of the three-trajectory realization in ranking calculations surpasses all existing end-point methods on this dataset. We further combine the three-trajectory realization with another promising modified end-point regime of varying the interior dielectric constant. The combined regime does not incur sizable improvements for ranks and deviations from experiment exhibit non-monotonic variations. [ABSTRACT FROM AUTHOR]

Machine learning was employed on the experimental crystal structures of the Cambridge Structural Database (CSD) to derive an intermolecular force field for all available types of atoms (general force field). The obtained pairwise interatomic potentials of the general force field allow for the fast and accurate calculation of intermolecular Gibbs energy. The approach is based on three postulates regarding Gibbs energy: the lattice energy must be below zero, the crystal structure must be a local minimum, and, if available, the experimental and the calculated lattice energy must coincide. The parametrized general force field was then validated regarding these three conditions. First, the experimental lattice energy was compared with the calculated energies. The observed errors were found to be in the order of experimental errors. Second, Gibbs lattice energy was calculated for all structures available in the CSD. Their energy values were found to be below zero in 99.86% of the cases. Finally, 500 random structures were minimized, and the change in density and energy was examined. The mean error in the case of density was below 4.06%, and for energy it was below 5.7%. The obtained general force field calculated Gibbs lattice energies of 259 041 known crystal structures within a few hours. Since Gibbs energy defines the reaction energy, the calculated energy can be used to predict chemical–physical properties of crystals, for instance, the formation of co‐crystals, polymorph stability and solubility. [ABSTRACT FROM AUTHOR]

Molecular representation learning is a crucial task to accelerate drug discovery and materials design. Graph neural networks (GNNs) have emerged as a promising approach to tackle this task. However, most of them do not fully consider the intramolecular interactions, i.e. bond stretching, angle bending, torsion, and nonbonded interactions, which are critical for determining molecular property. Recently, a growing number of 3D-aware GNNs have been proposed to cope with the issue, while these models usually need large datasets and accurate spatial information. In this work, we aim to design a GNN which is less dependent on the quantity and quality of datasets. To this end, we propose a force field-inspired neural network (FFiNet), which can include all the interactions by incorporating the functional form of the potential energy of molecules. Experiments show that FFiNet achieves state-of-the-art performance on various molecular property datasets including both small molecules and large protein–ligand complexes, even on those datasets which are relatively small and without accurate spatial information. Moreover, the visualization for FFiNet indicates that it automatically learns the relationship between property and structure, which can promote an in-depth understanding of molecular structure. [ABSTRACT FROM AUTHOR]

Hydration free energies of small molecules are commonly used as benchmarks for solvation models. However, errors in predicting hydration free energies are partially due to the force fields used and not just the solvation model. To address this, we have used the 3D reference interaction site model (3D-RISM) of molecular solvation and existing benchmark explicit solvent calculations with a simple element count correction (ECC) to identify problems with the non-bond parameters in the general AMBER force field (GAFF). 3D-RISM was used to calculate hydration free energies of all 642 molecules in the FreeSolv database, and a partial molar volume correction (PMVC), ECC, and their combination (PMVECC) were applied to the results. The PMVECC produced a mean unsigned error of 1.01 ± 0.04 kcal / mol and root mean squared error of 1.44 ± 0.07 kcal / mol , better than the benchmark explicit solvent calculations from FreeSolv, and required less than 15 s of computing time per molecule on a single CPU core. Importantly, parameters for PMVECC showed systematic errors for molecules containing Cl, Br, I, and P. Applying ECC to the explicit solvent hydration free energies found the same systematic errors. The results strongly suggest that some small adjustments to the Lennard–Jones parameters for GAFF will lead to improved hydration free energy calculations for all solvent models. [ABSTRACT FROM AUTHOR]

Charge scaling as an effective solution to the experiment–computation disagreement in molecular modelling of ionic liquids (ILs) could bring the computational results close to the experimental reference for various thermodynamic properties. According to the large-scale benchmark calculations of mass density, solvation, and water-ILs transfer-free energies in our series of papers, the charge-scaling factor of 0.8 serves as a near-optimal option generally applicable to most ILs, although a system-dependent parameter adjustment could be attempted for further improved performance. However, there are situations in which such a charge-scaling treatment would fail. Namely, charge scaling cannot really affect the simulation outcome, or minimally perturbs the results that are still far from the experimental value. In such situations, the vdW radius as an additional adjustable parameter is commonly tuned to minimize the experiment–calculation deviation. In the current work, considering two ILs from the quinuclidinium family, we investigate the impacts of this vdW-scaling treatment on the mass density and the solvation/partition thermodynamics in a fashion similar to our previous charge-scaling works, i.e., scanning the vdW-scaling factor and computing physical properties under these parameter sets. It is observed that the mass density exhibits a linear response to the vdW-scaling factor with slopes close to −1.8 g/mL. By further investigating a set of physiochemically relevant temperatures between 288 K and 348 K, we confirm the robustness of the vdW-scaling treatment in the estimation of bulk properties. The best vdW-scaling parameter for mass density would worsen the computation of solvation/partition thermodynamics, and a marginal decrease in the vdW-scaling factor is considered as an intermediate option balancing the reproductions of bulk properties and solvation thermodynamics. These observations could be understood in a way similar to the charge-scaling situation. i.e., overfitting some properties (e.g., mass density) would degrade the accuracy of the other properties (e.g., solvation free energies). Following this principle, the general guideline for applying this vdW-tuning protocol is by using values between the density-derived choice and the solvation/partition-derived solution. The charge and current vdW scaling treatments cover commonly encountered ILs, completing the protocol for accurate modelling of ILs with fixed-charge force fields. [ABSTRACT FROM AUTHOR]

Molecular dynamic (MD) simulations offer a way to study biomolecular interactions and their dynamics at the atomistic level. There are only a few studies of RNA-protein complexes in MD simulations, and here we wanted to study how force fields differ when simulating RNA-protein complexes: 1) argonaute 2 with bound guide RNA and a target RNA, 2) CasPhi-2 bound to CRISPR RNA and 3) Retinoic acid-inducible gene I C268F variant in complex with double-stranded RNA. We tested three non-polarizable force fields: Amber protein force fields ff14SB and ff19SB with RNA force field OL3, and the all-atom OPLS4 force field. Due to the highly charged and polar nature of RNA, we also tested the polarizable AMOEBA force field and the ff19SB and OL3 force fields with a polarizable water model O3P. Our results show that the non-polarizable force fields lead to compact and stable complexes. The polarizability in the force field or in the water model allows significantly more movement from the complex, but in some cases, this results in the disintegration of the complex structure, especially if the protein contains longer loop regions. Thus, one should be cautious when running long-scale simulations with polarizability. As a conclusion, all the tested force fields can be used to simulate RNA-protein complexes and the choice of the optimal force field depends on the studied system and research question.

Based on the TraPPE force field, previously unknown values of the parameters of the intermolecular interaction potential of trans-decalin were determined. Parametrization was carried out using experimental data on saturated vapor pressure and density at atmospheric pressure. The found parameters make it possible to adequately describe the boiling and condensation lines of trans-decalin and also predict the critical values of pressure, density, and temperature with satisfactory accuracy. Calculations of vapor-liquid phase equilibrium conditions for a binary CO2—trans-decalin mixture in supercritical conditions for CO2 were carried out. When quantitatively comparing the calculated values with experimental data, an underestimation of pressure at a temperature of 345.4 K by 30% is observed, which decreases to 5% for temperatures up to 525 K.

"Plant-type" ferredoxins (Fds) in the thylakoid membranes of plants, algae, and cyanobacteria possess a single [2Fe-2S] cluster in active sites and mediate light-induced electron transfer from Photosystem I reaction centers to various Fd-dependent enzymes. Structural knowledge of plant-type Fds is relatively limited to static structures, and the detailed behavior of oxidized and reduced Fds has not been fully elucidated. It is important that the investigations of the effects of active-center reduction on the structures and dynamics for elucidating electron-transfer mechanisms. In this study, model systems of oxidized and reduced Fds were constructed from the high-resolution crystal structure of Chlamydomonas reinhardtii Fd1, and three 200 ns molecular dynamics simulations were performed for each system. The force field parameters of the oxidized and reduced active centers were independently obtained using quantum chemical calculations. There were no substantial differences in the global conformations of the oxidized and reduced forms. In contrast, active-center reduction affected the hydrogen-bond network and compactness of the surrounding residues, leading to the increased flexibility of the side chain of Phe61, which is essential for the interaction between Fd and the target protein. These computational results will provide insight into the electron-transfer mechanisms in the Fds. [ABSTRACT FROM AUTHOR]

The euhedral shapes of pyrite FeS2 are usually exposing three main surfaces: striated (001), smooth (111), and striated (210), leading to the cubical, octahedral, and pyritohedral morphology, respectively. The macroscopic striations, sometimes called triglyphs on cubic crystals, are parallel on specific surfaces and aligned to the <100> directions. Other types of striated and unstriated (hkl) surfaces can be observed on pyrite crystals from Peru, a country offering a rich diversity of pyrite shapes. A rare specimen from Elba Island (Italy) is a pyritohedron with uncommon directions of striations (so-called "negative" striations, first described in Japanese minerals). The Wulf kinetic growth and the periodic bond chain (PBC) theories were not relevant enough to explain crystal shapes, the texture of the surfaces, and twinning. To bring some new insights on crystal growth, twinning, and anisotropy, pyrite samples are analyzed using XRD, SEM, and EDS techniques coupled with atomistic simulations. A first analysis points out that sulfur terminations play a key role in the growth of striations in distinguishing the six <001> directions. The negative striated pyritohedral pyrite would be, in fact, a special case that has stabilized the {120} surfaces, which are structurally different from the {210} facets. The {120} surface has a slightly higher surface energy than the {210} surface (surface energies of 1.68 and 1.65 J/m2, respectively, calculated with force field methods). {120} pyritohedra from Elba, Italy, are growing next to micaceous iron oxides (a type of hematite), which are also peculiar specimens with magnetic properties. Another specificity is that some rare earth elements have been identified in the pyrite sample from Elba, which leads to a hypothesis that geothermal conditions favor "negative" striations (e.g., discernible in Akita prefecture-Japan, Boyacá-Colombia, and Cassandra-Greece). The striation directions become useful to distinguish (hkl) surfaces and to identify twinning as they follow the same patterns on each interpenetrated crystal. The most common twinning is the "iron cross," a penetration twin of two crystals defined by a rotation of 90° along an [001] axis with a coincidence in the iron sub-lattice (e.g., twinning by merohedry) and with a twin center. The sulfur network also plays a fundamental role in stabilizing the (001) interface and in keeping the chemical bulk properties at the boundary, as confirmed by additional ab initio simulations. The grain boundary is a 2D defect in which the (001) twinning is relatively stable as it is common. The calculated formation energy of the rotation twinning is 0.8 J/m2. The rotation twinning is associated with an apparent reflection on (110) planes. The formation energy of the (110) mirror grain boundary is 1.7 J/m2, and the interface at the atomic scale is relatively uniform in agreement with experimental observations. [ABSTRACT FROM AUTHOR]

This paper considers the narrow escape problem of a Brownian particle within a three‐dimensional Riemannian manifold under the influence of the force field. We compute an asymptotic expansion of mean sojourn time for Brownian particles. As a auxiliary result, we obtain the singular structure for the restricted Neumann Green's function which may be of independent interest. [ABSTRACT FROM AUTHOR]

Pupil diameter, under constant illumination, is known to reflect individuals' internal states, such as surprise about observation and environmental uncertainty. Despite the growing use of pupillometry in cognitive learning studies as an additional measure for examining internal states, few studies have used pupillometry in human motor learning studies. Here, we provide the first detailed characterization of pupil diameter changes in a short-term reach adaptation paradigm. We measured pupil changes in 121 human participants while they adapted to abrupt, gradual, or switching force field conditions. Sudden increases in movement error caused by the introduction/reversal of the force field resulted in strong phasic pupil dilation during movement accompanied by a transient increase in tonic premovement baseline pupil diameter in subsequent trials. In contrast, pupil responses were reduced when the force field was gradually introduced, indicating that large, unexpected errors drove the changes in pupil responses. Interestingly, however, error-induced pupil responses gradually became insensitive after experiencing multiple force field reversals. We also found an association between baseline pupil diameter and incidental knowledge of the gradually introduced perturbation. Finally, in all experiments, we found a strong co-occurrence of larger baseline pupil diameter with slower reaction and movement times after each rest break. Collectively, these results suggest that tonic baseline pupil diameter reflects one's belief about environmental uncertainty, whereas phasic pupil dilation during movement reflects surprise about a sensory outcome (i.e., movement error), and both effects are modulated by novelty. Our results provide a new approach for nonverbally assessing participants' internal states during motor learning. [ABSTRACT FROM AUTHOR]

The longevity of asphalt pavements is a key focus of road engineering, which closely relates to the self-healing ability of bitumen. Our work aims to establish a CGMD model and matched force field for bitumen and break through the limitations of the research scale to further explore the microscopic mechanism of bitumen self-healing. In this study, a CGMD mapping scheme containing 16 kinds of beads is proposed, and the non-bond potential energy function and bond potential energy function are calculated based on all-atom simulation to construct and validate a coarse-grained model for bitumen. On this basis, a micro-crack model with a width of 36.6nm is simulated, and the variation laws of potential energy, density, diffusion coefficient, relative concentration and temperature in the process of bitumen self-healing are analyzed with the cracking rate parameter proposed to characterize the degree of bitumen crack healing. The results show that the computational size of the coarse-grained simulation is much larger than that of the all-atom, which can explain the self-healing mechanism at the molecular level. In the self-healing process, non-bonded interactions dominate the molecular movement, and differences in the decreased rate of diffusion among the components indicate that saturates and aromatics play a major role in self-healing. Meanwhile, the variations in crack rates reveal that healing time is inversely proportional to temperature. The impact of increasing temperature on reducing healing time is most obvious when the temperature approaches the glass transition temperature (300 K). [ABSTRACT FROM AUTHOR]