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In this paper we investigate the adequacy of deep learning force field models for modeling amorphous bodies. A polymer with the studied physical properties, polyphenylene sulfide, was chosen as a test substance. The simulation results shows that the forces predicted by neural networks acting on polymer atoms are significantly different from the forces calculated by ab initio molecular dynamics methods. A qualitative comparison with the force field model of a simpler compound, black phosphorene, shows that feedforward neural networks are unsuitable for modeling complex amorphous substances. [ABSTRACT FROM AUTHOR]

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

Bimanual in-phase and anti-phase coordination modes represent two basic movement patterns with distinct characteristics-homologous muscle contraction and non-homologous muscle contraction, respectively. A method to understand the contribution of each limb to the overall coordination pattern involves detuning (Δω) the natural eigenfrequency of each limb. In the present experiment, we experimentally broke the symmetry between the two upper limbs by adding elastic and viscous force fields using a Kinarm robot exoskeleton. We measured the effect of this symmetry breaking on coordination stability as participants performed bimanual in-phase and anti-phase movements using their left and right hand in 1:1 frequency locking mode. Differences between uncoupled frequencies were manipulated via the application of viscous & elastic force fields and using fast and slow oscillation frequencies with a custom task developed using the Kinarm robotic exoskeleton. The effects of manipulating the asymmetry between the limbs were measured through the mean and variability of relative phase (ϕ) from the intended modes of 0 ° or 180 °. In general, participants deviated less from intended phase irrespective of coordination mode in all matched conditions, except for when elastic loads are applied to both arms in the anti-phase coordination. Second, we found that when force fields were mismatched participants exhibited a larger deviation from the intended phase. Overall, there was increased phase deviation during anti-phase coordination. Finally, participants exhibited higher variability in relative phase in mismatched force conditions compared to matched force conditions, with overall higher variability during anti-phase coordination mode. We extend previous research by demonstrating that symmetry breaking caused by force differences between the limbs disrupts stability in each coordination mode.

Abstract Neural network force fields have significantly advanced ab initio atomistic simulations across diverse fields. However, their application in the realm of magnetic materials is still in its early stage due to challenges posed by the subtle magnetic energy landscape and the difficulty of obtaining training data. Here we introduce a data-efficient neural network architecture to represent density functional theory total energy, atomic forces, and magnetic forces as functions of atomic and magnetic structures. Our approach incorporates the principle of equivariance under the three-dimensional Euclidean group into the neural network model. Through systematic experiments on various systems, including monolayer magnets, curved nanotube magnets, and moiré-twisted bilayer magnets of CrI3, we showcase the method’s high efficiency and accuracy, as well as exceptional generalization ability. The work creates opportunities for exploring magnetic phenomena in large-scale materials systems.

Predicting the structure and dynamics of RNA molecules still proves challenging because of the relative scarcity of experimental RNA structures on which to train models and the very sensitive nature of RNA towards its environment. In the last decade, several atomistic force fields specifically designed for RNA have been proposed and are commonly used for simulations. However, it is not necessarily clear which force field is the most suitable for a given RNA molecule. In this contribution, we propose the use of the computational energy landscape framework to explore the energy landscape of RNA systems as it can bring complementary information to the more standard approaches of enhanced sampling simulations based on molecular dynamics. We apply the EL framework to the study of a small RNA pseudoknot, the Aquifex aeolicus tmRNA pseudoknot PK1, and we compare the results of five different RNA force fields currently available in the AMBER simulation software, in implicit solvent. With this computational approach, we can not only compare the predicted 'native' states for the different force fields, but the method enables us to study metastable states as well. As a result, our comparison not only looks at structural features of low energy folded structures, but provides insight into folding pathways and higher energy excited states, opening to the possibility of assessing the validity of force fields also based on kinetics and experiments providing information on metastable and unfolded states. [ABSTRACT FROM AUTHOR]

Covalent adaptable networks and vitrimers are novel polymers with dynamic reversible bond exchange reactions for crosslinks, enabling them to modulate their properties between those of thermoplastics and thermosets. They have been gathering interest as materials for their recycling and self-healing properties. In this review, we discuss different molecular simulation efforts that have been used over the last decade to investigate and understand the nanoscale and molecular behaviors of covalent adaptable networks and vitrimers. In particular, molecular dynamics, Monte Carlo, and a hybrid of molecular dynamics and Monte Carlo approaches have been used to model the dynamic bond exchange reaction, which is the main mechanism of interest since it controls both the mechanical and rheological behaviors. The molecular simulation techniques presented yield sufficient results to investigate the structure and dynamics as well as the mechanical and rheological responses of such dynamic networks. The benefits of each method have been highlighted. The use of other tools such as theoretical models and machine learning has been included. We noticed, amongst the most prominent results, that stress relaxes as the bond exchange reaction happens, and that at temperatures higher than the glass transition temperature, the self-healing properties are better since more bond BERs are observed. The lifetime of dynamic covalent crosslinks follows, at moderate to high temperatures, an Arrhenius-like temperature dependence. We note the modeling of certain properties like the melt viscosity with glass transition temperature and the topology freezing transition temperature according to a behavior ruled by either the Williams–Landel–Ferry equation or the Arrhenius equation. Discrepancies between the behavior in dissociative and associative covalent adaptable networks are discussed. We conclude by stating which material parameters and atomistic factors, at the nanoscale, have not yet been taken into account and are lacking in the current literature. [ABSTRACT FROM AUTHOR]

Fullerenes, particularly C60, exhibit unique properties that make them promising candidates for various applications, including drug delivery and nanomedicine. However, their interactions with biomolecules, especially proteins, remain not fully understood. This study implements both explicit and implicit C60 models into the UNRES coarse-grained force field, enabling the investigation of fullerene–protein interactions without the need for restraints to stabilize protein structures. The UNRES force field offers computational efficiency, allowing for longer timescale simulations while maintaining accuracy. Five model proteins were studied: FK506 binding protein, HIV-1 protease, intestinal fatty acid binding protein, PCB-binding protein, and hen egg-white lysozyme. Molecular dynamics simulations were performed with and without C60 to assess protein stability and investigate the impact of fullerene interactions. Analysis of contact probabilities reveals distinct interaction patterns for each protein. FK506 binding protein (1FKF) shows specific binding sites, while intestinal fatty acid binding protein (1ICN) and uteroglobin (1UTR) exhibit more generalized interactions. The explicit C60 model shows good agreement with all-atom simulations in predicting protein flexibility, the position of C60 in the binding pocket, and the estimation of effective binding energies. The integration of explicit and implicit C60 models into the UNRES force field, coupled with recent advances in coarse-grained modeling and multiscale approaches, provides a powerful framework for investigating protein–nanoparticle interactions at biologically relevant scales without the need to use restraints stabilizing the protein, thus allowing for large conformational changes to occur. These computational tools, in synergy with experimental techniques, can aid in understanding the mechanisms and consequences of nanoparticle–biomolecule interactions, guiding the design of nanomaterials for biomedical applications. [ABSTRACT FROM AUTHOR]

Neural network force fields have significantly advanced ab initio atomistic simulations across diverse fields. However, their application in the realm of magnetic materials is still in its early stage due to challenges posed by the subtle magnetic energy landscape and the difficulty of obtaining training data. Here we introduce a data-efficient neural network architecture to represent density functional theory total energy, atomic forces, and magnetic forces as functions of atomic and magnetic structures. Our approach incorporates the principle of equivariance under the three-dimensional Euclidean group into the neural network model. Through systematic experiments on various systems, including monolayer magnets, curved nanotube magnets, and moiré-twisted bilayer magnets of CrI3, we showcase the method's high efficiency and accuracy, as well as exceptional generalization ability. The work creates opportunities for exploring magnetic phenomena in large-scale materials systems. [ABSTRACT FROM AUTHOR]

The subject of the study is the problem of precession of a gyrostat with a fixed point in three homogeneous force fields. The class of precessional movements under consideration is characterized by the properties of constancy of the nutation angle and commensurability of the precession rates and the gyrostat's own rotation. The equations of motion of the gyrostat are reduced to three second-order differential equations with respect to the precession rates and the proper rotation of the gyrostat. Integration of these equations was carried out in the case of precessional-isoconic movements (the speeds of precession and proper rotation are equal) and in one case the resonant values of the speeds of precession and proper rotation (the precession speed is twice the speed of proper rotation – resonance 2 : 1). It is proven that the solutions obtained in the article are characterized by elementary functions of time. [ABSTRACT FROM AUTHOR]

Bio-based semicrystalline polylactide (PLA) has a growing value as a substitute for fossil-based polyesters in technical applications and as a biocompatible material in medicine. The complexity of the behavior of PLA, determined by the presence of different stereoisomers, the role of electrostatic interactions, and its slow crystallization rate, makes computer simulations an important tool to discover new approaches to control the properties of PLA-based materials. The goal of this review is to summarize the efforts to simulate PLA materials with different levels of detail, including the quantum mechanical approach, all-atom modeling, and coarse-grained particle models. We focus on the validation of the models and the ways to cross-check the results with other simulation and experimental data. Special attention is devoted to the simulations of PLA in the presence of water, which provide insights into molecular mechanisms of hydrolytic degradation of PLA, especially at the initial stage, when the structural changes can not yet be detected by experimental methods. Ultimately, the selection of the appropriate simulation methods can facilitate material design, by combining the throughput and level of detail necessary for the job. [ABSTRACT FROM AUTHOR]

We selected 145 reference organic molecules that include model fragments used in computer-aided drug design. We calculated 158 conformational energies and barriers using force fields, with wide applicability in commercial and free softwares and extensive application on the calculation of conformational energies of organic molecules, e.g. the UFF and DREIDING force fields, the Allinger's force fields MM3-96, MM3-00, MM4-8, the MM2-91 clones MMX and MM+, the MMFF94 force field, MM4, ab initio Hartree–Fock (HF) theory with different basis sets, the standard density functional theory B3LYP, the second-order post-HF MP2 theory and the Domain-based Local Pair Natural Orbital Coupled Cluster DLPNO-CCSD(T) theory, with the latter used for accurate reference values. The data set of the organic molecules includes hydrocarbons, haloalkanes, conjugated compounds, and oxygen-, nitrogen-, phosphorus- and sulphur-containing compounds. We reviewed in detail the conformational aspects of these model organic molecules providing the current understanding of the steric and electronic factors that determine the stability of low energy conformers and the literature including previous experimental observations and calculated findings. While progress on the computer hardware allows the calculations of thousands of conformations for later use in drug design projects, this study is an update from previous classical studies that used, as reference values, experimental ones using a variety of methods and different environments. The lowest mean error against the DLPNO-CCSD(T) reference was calculated for MP2 (0.35 kcal mol−1), followed by B3LYP (0.69 kcal mol−1) and the HF theories (0.81–1.0 kcal mol−1). As regards the force fields, the lowest errors were observed for the Allinger's force fields MM3-00 (1.28 kcal mol−1), ΜΜ3-96 (1.40 kcal mol−1) and the Halgren's MMFF94 force field (1.30 kcal mol−1) and then for the MM2-91 clones MMX (1.77 kcal mol−1) and MM+ (2.01 kcal mol−1) and MM4 (2.05 kcal mol−1). The DREIDING (3.63 kcal mol−1) and UFF (3.77 kcal mol−1) force fields have the lowest performance. These model organic molecules we used are often present as fragments in drug-like molecules. The values calculated using DLPNO-CCSD(T) make up a valuable data set for further comparisons and for improved force field parameterization. [ABSTRACT FROM AUTHOR]

Quantum chemistry in the form of density functional theory (DFT) calculations is a powerful numerical experiment for predicting intermolecular interaction energies. However, no chemical insight is gained in this way beyond predictions of observables. Energy decomposition analysis (EDA) can quantitatively bridge this gap by providing values for the chemical drivers of the interactions, such as permanent electrostatics, Pauli repulsion, dispersion, and charge transfer. These energetic contributions are identified by performing DFT calculations with constraints that disable components of the interaction. This review describes the second-generation version of the absolutely localized molecular orbital EDA (ALMO-EDA-II). The effects of different physical contributions on changes in observables such as structure and vibrational frequencies upon complex formation are characterized via the adiabatic EDA. Example applications include red- versus blue-shifting hydrogen bonds; the bonding and frequency shifts of CO, N2, and BF bound to a [Ru(II)(NH3)5]2 + moiety; and the nature of the strongly bound complexes between pyridine and the benzene and naphthalene radical cations. Additionally, the use of ALMO-EDA-II to benchmark and guide the development of advanced force fields for molecular simulation is illustrated with the recent, very promising, MB-UCB potential.

The ability of various interatomic potentials to reproduce the properties of silicene, that is, 2D single-layer silicon, polymorphs was examined. Structural and mechanical properties of flat, low-buckled, trigonal dumbbell, honeycomb dumbbell, and large honeycomb dumbbell silicene phases, were obtained using density functional theory and molecular statics calculations with Tersoff, MEAM, Stillinger–Weber, EDIP, ReaxFF, COMB, and machine-learning-based interatomic potentials. A quantitative systematic comparison and a discussion of the results obtained are reported.

Many pairwise additive force fields are in active use for intrinsically disordered proteins (IDPs) and regions (IDRs), some of which modify energetic terms to improve the description of IDPs/IDRs but are largely in disagreement with solution experiments for the disordered states. This work considers a new direction-the connection to configurational entropy-and how it might change the nature of our understanding of protein force field development to equally well encompass globular proteins, IDRs/IDPs, and disorder-to-order transitions. We have evaluated representative pairwise and many-body protein and water force fields against experimental data on representative IDPs and IDRs, a peptide that undergoes a disorder-to-order transition, for seven globular proteins ranging in size from 130 to 266 amino acids. We find that force fields with the largest statistical fluctuations consistent with the radius of gyration and universal Lindemann values for folded states simultaneously better describe IDPs and IDRs and disorder-to-order transitions. Hence, the crux of what a force field should exhibit to well describe IDRs/IDPs is not just the balance between protein and water energetics but the balance between energetic effects and configurational entropy of folded states of globular proteins.

Many molecular simulation methods use force fields to help model and simulate molecules and their behavior in various environments. Force fields are sets of functions and parameters used to calculate the potential energy of a chemical system as a function of the atomic coordinates. Despite the widespread use of force fields, their inadequacies are often thought to contribute to systematic errors in molecular simulations. Furthermore, different force fields tend to give varying results on the same systems with the same simulation settings. Here, we present a pipeline for comparing the geometries of small molecule conformers. We aimed to identify molecules or chemistries that are particularly informative for future force field development because they display inconsistencies between force fields. We applied our pipeline to a subset of the eMolecules database, and highlighted molecules that appear to be parameterized inconsistently across different force fields. We then identified over-represented functional groups in these molecule sets. The molecules and moieties identified by this pipeline may be particularly helpful for future force field parameterization.

Fullerenes, particularly C60, exhibit unique properties that make them promising candidates for various applications, including drug delivery and nanomedicine. However, their interactions with biomolecules, especially proteins, remain not fully understood. This study implements both explicit and implicit C60 models into the UNRES coarse-grained force field, enabling the investigation of fullerene–protein interactions without the need for restraints to stabilize protein structures. The UNRES force field offers computational efficiency, allowing for longer timescale simulations while maintaining accuracy. Five model proteins were studied: FK506 binding protein, HIV-1 protease, intestinal fatty acid binding protein, PCB-binding protein, and hen egg-white lysozyme. Molecular dynamics simulations were performed with and without C60 to assess protein stability and investigate the impact of fullerene interactions. Analysis of contact probabilities reveals distinct interaction patterns for each protein. FK506 binding protein (1FKF) shows specific binding sites, while intestinal fatty acid binding protein (1ICN) and uteroglobin (1UTR) exhibit more generalized interactions. The explicit C60 model shows good agreement with all-atom simulations in predicting protein flexibility, the position of C60 in the binding pocket, and the estimation of effective binding energies. The integration of explicit and implicit C60 models into the UNRES force field, coupled with recent advances in coarse-grained modeling and multiscale approaches, provides a powerful framework for investigating protein–nanoparticle interactions at biologically relevant scales without the need to use restraints stabilizing the protein, thus allowing for large conformational changes to occur. These computational tools, in synergy with experimental techniques, can aid in understanding the mechanisms and consequences of nanoparticle–biomolecule interactions, guiding the design of nanomaterials for biomedical applications.

An Online tool for Fragment-based Molecule Parametrization (OFraMP) is described. OFraMP is a web application for assigning atomic interaction parameters to large molecules by matching sub-fragments within the target molecule to equivalent sub-fragments within the Automated Topology Builder (ATB, atb.uq.edu.au) database. OFraMP identifies and compares alternative molecular fragments from the ATB database, which contains over 890,000 pre-parameterized molecules, using a novel hierarchical matching procedure. Atoms are considered within the context of an extended local environment (buffer region) with the degree of similarity between an atom in the target molecule and that in the proposed match controlled by varying the size of the buffer region. Adjacent matching atoms are combined into progressively larger matched sub-structures. The user then selects the most appropriate match. OFraMP also allows users to manually alter interaction parameters and automates the submission of missing substructures to the ATB in order to generate parameters for atoms in environments not represented in the existing database. The utility of OFraMP is illustrated using the anti-cancer agent paclitaxel and a dendrimer used in organic semiconductor devices. OFraMP applied to paclitaxel (ATB ID 35922). [ABSTRACT FROM AUTHOR]

Quantum mechanics/molecular mechanics (QM/MM) hybrid models allow one to address chemical phenomena in complex molecular environments. Whereas this modeling approach can cope with a large system size at moderate computational costs, the models are often tedious to construct and require manual preprocessing and expertise. As a result, transferability to new application areas can be limited and the many parameters are not easy to adjust to reference data that are typically scarce. Therefore, it is desirable to devise automated procedures of controllable accuracy, which enables such modeling in a standardized and black‐box‐type manner. Although diverse best‐practice protocols have been set up for the construction of individual components of a QM/MM model (e.g., the MM potential, the type of embedding, the choice of the QM region), automated procedures that reconcile all steps of the QM/MM model construction are still rare. Here, we review the state of the art of QM/MM modeling with a focus on automation. We elaborate on MM model parametrization, on atom‐economical physically‐motivated QM region selection, and on embedding schemes that incorporate mutual polarization as critical components of the QM/MM model. In view of the broad scope of the field, we mostly restrict the discussion to methodologies that build de novo models based on first‐principles data, on uncertainty quantification, and on error mitigation with a high potential for automation. Ultimately, it is desirable to be able to set up reliable QM/MM models in a fast and efficient automated way without being constrained by specific chemical or technical limitations. This article is categorized under:Electronic Structure Theory > Combined QM/MM Methods [ABSTRACT FROM AUTHOR]

Functionalized nanotubes (NTs), nanosheets, nanorods, and porous organometallic scaffolds are potential in vivo carriers for cancer therapeutics. Precise delivery through these agents depends on factors like hydrophobicity, payload capacity, bulk/surface adsorption, orientation of molecules inside the host matrix, bonding, and nonbonding interactions. Herein, we summarize advances in simulation techniques, which are extremely valuable in initial geometry optimization and evaluation of the loading and unloading behavior of encapsulated drug molecules. Computational methods broadly involve the use of quantum and classical mechanics for studying the behavior of molecular properties. Combining theoretical processes with experimental techniques, such as X-ray crystallography, NMR spectroscopy, and bioassays, can provide a more comprehensive understanding of the structure and function of biological molecules. This integrated approach has led to numerous breakthroughs in drug discovery, enzyme design, and the study of complex biological processes. This short review provides an overview of results and challenges described from erstwhile investigations on the molecular interaction of anticancer drugs with nanocarriers of different aspect ratios.

We present a new benchmark set consisting of 16 large non-covalently bound systems (LNCI16) ranging from 380 up to 1988 atoms and featuring diverse interaction motives. Gas-phase interaction energies are calculated with various composite DFT, semi-empirical quantum mechanical (SQM), and force field (FF) methods and are evaluated using accurate DFT reference values. Of the employed QM methods, PBEh-3c proves to be the most robust for large systems with a relative mean absolute deviation (relMAD) of 8.5% with respect to the reference interaction energies. r2 SCAN-3c yields an even smaller relMAD, at least for the subset of complexes for which the calculation could be converged, but is less robust for systems with smaller HOMO–LUMO gaps. The inclusion of Fock-exchange is therefore important for the description of very large non-covalent interaction (NCI) complexes in the gas phase. GFN2-xTB was found to be the best performer of the SQM methods with an excellent result of only 11.1% deviation. From the assessed force fields, GFN-FF and GAFF achieve the best accuracy. Considering their low computational costs, both can be recommended for routine calculations of very large NCI complexes, with GFN-FF being clearly superior in terms of general applicability. Hence, GFN-FF may be routinely applied in supramolecular synthesis planning. 1 Introduction 2 The LNCI16 Benchmark Set 3 Computational Details 4 Generation of Reference Values 5 Results and Discussion 6 Conclusions [ABSTRACT FROM AUTHOR]

This paper is on determination of the thermal conductivities of several hydrocarbon base oils by means of non-equilibrium molecular dynamics simulations using two different force fields. It aims to explore a simulation-based method for lubricant molecular design and analysis concerning heat transfer in electrical vehicle lubrication. Argon was analyzed as a reference for method evaluation, and the results reveal that the calculated conductivity strongly depends on the size of the computational domain. However, for hydrocarbon base oils, the dependence on computation domain size is less prominent as the domain size increases. The method of direct calculation in a sufficiently large computation domain and that of reciprocal extrapolation with data calculated in a much smaller domain are both applicable, and each has a certain value in oil conductivity calculation. The calculated conductivities show certain overpredictions when compared with experimentally measured results, and the overprediction factor is related to number of carbon atoms of the liquid molecules. The results reveal that the thermal conductivity of a single-chain hydrocarbon liquid is linearly proportional to the number of carbon atoms. While each additional branch increases thermal conductivity slightly, the presence of multiple branches reduces it from the ideal linear relationship. A set of equations was formulated to correlate hydrocarbon liquid thermal conductivity with molecular characteristics in terms of number of carbon atoms and number of branches. [ABSTRACT FROM AUTHOR]

The article briefly reviews the history of the development of ideas about the dynamics of proteins and other biopolymers and notes the significant contribution of V.I. Goldansky in organizing and conducting these studies in Russia. The modern development of earlier ideas about the dynamics of biopolymers and protein folding is discussed. It is shown that folding is not an isolated problem and is related to the fundamental dynamic properties of linear polymers in the condensed phase. Analytical methods using approaches based on multidimensional geometry show that the viscosity of the medium is one of the most important factors that determines the rules for the movement of a representative point along the ultramultidimensional potential energy surface (PES). These rules lead to the concentration of trajectories in those regions of the configuration space of a macromolecule that correspond to relatively smooth PES regions, which is important for understanding the reasons for the stability of the results of calculations of large systems using the molecular dynamics (MD) method, despite the fundamental inaccuracy in determining the available force fields. This article also briefly describes a new approach to determine and study the properties of a multidimensional PES, which is based on the features of the topology of the configuration space of linear polymers (and biopolymers), symmetry with respect to permutations of identical chain links, and Morse theory for studying the topography of multidimensional surfaces. Under certain conditions, this approach gives observable analytical results for the topography of the PES and the free energy surface (FES) of a macromolecule and makes it possible to relate the rather heterogeneous results of experiments on protein folding from a unified point of view. At the same time, a new formulation appears for a number of fundamental and controversial issues related to the physical laws of the formation of living systems. In particular, a connection is traced between the temperature regime on the planet and the chemical realization of the energy of nonvalent interactions in a macromolecule, which are necessary for the formation of unique spatial structures of biopolymers. [ABSTRACT FROM AUTHOR]

Dynamic adaptation is an error-driven process of adjusting planned motor actions to changes in task dynamics (Shadmehr, 2017). Adapted motor plans are consolidated into memories that contribute to better performance on re-exposure. Consolidation begins within 15 min following training (Criscimagna-Hemminger and Shadmehr, 2008), and can be measured via changes in resting state functional connectivity (rsFC). For dynamic adaptation, rsFC has not been quantified on this timescale, nor has its relationship to adaptative behavior been established. We used a functional magnetic resonance imaging (fMRI)-compatible robot, the MR-SoftWrist (Erwin et al., 2017), to quantify rsFC specific to dynamic adaptation of wrist movements and subsequent memory formation in a mixed-sex cohort of human participants. We acquired fMRI during a motor execution and a dynamic adaptation task to localize brain networks of interest, and quantified rsFC within these networks in three 10-min windows occurring immediately before and after each task. The next day, we assessed behavioral retention. We used a mixed model of rsFC measured in each time window to identify changes in rsFC with task performance, and linear regression to identify the relationship between rsFC and behavior. Following the dynamic adaptation task, rsFC increased within the cortico-cerebellar network and decreased interhemispherically within the cortical sensorimotor network. Increases within the cortico-cerebellar network were specific to dynamic adaptation, as they were associated with behavioral measures of adaptation and retention, indicating that this network has a functional role in consolidation. Instead, decreases in rsFC within the cortical sensorimotor network were associated with motor control processes independent from adaptation and retention. [ABSTRACT FROM AUTHOR]

Atomic-level understanding of the dynamic feature of host–guest interactions remains a central challenge in supramolecular chemistry. The remarkable guest binding behavior of the Cucurbiturils family of supramolecular containers makes them promising drug carriers. Among Cucurbit[n]urils, Cucurbit[8]uril (CB8) has an intermediate portal size and cavity volume. It can exploit almost all host–guest recognition motifs formed by this host family. In our previous work, an extensive computational investigation of the binding of seven commonly abused and structurally diverse drugs to the CB8 host was performed, and a general dynamic binding picture of CB8-guest interactions was obtained. Further, two widely used fixed-charge models for drug-like molecules were investigated and compared in great detail, aiming at providing guidelines in choosing an appropriate charge scheme in host-guest modelling. Iterative refitting of atomic charges leads to improved binding thermodynamics and the best root-mean-squared deviation from the experimental reference is 2.6 kcal/mol. In this work, we focus on a thorough evaluation of the remaining parts of classical force fields, i.e., the bonded interactions. The widely used general Amber force fields are assessed and refitted with generalized force-matching to improve the intra-molecular conformational preference, and thus the description of inter-molecular host–guest interactions. The interaction pattern and binding thermodynamics show a significant dependence on the modelling parameters. The refitted system-specific parameter set improves the consistency of the modelling results and the experimental reference significantly. Finally, combining the previous charge-scheme comparison and the current force-field refitting, we provide general guidelines for the theoretical modelling of host–guest binding. [ABSTRACT FROM AUTHOR]

The manufacturing of high-modulus, high-strength fibers is of paramount importance for real-world, high-end applications. In this respect, carbon nanotubes represent the ideal candidates for realizing such fibers. However, their remarkable mechanical performance is difficult to bring up to the macroscale, due to the low load transfer within the fiber. A strategy to increase such load transfer is the introduction of chemical linkers connecting the units, which can be obtained, for example, using carbon ion-beam irradiation. In this work, we investigate, via molecular dynamics simulations, the mechanical properties of twisted nanotube bundles in which the linkers are composed of interstitial single carbon atoms. We find a significant interplay between the twist and the percentage of linkers. Finally, we evaluate the suitability of two different force fields for the description of these systems: the dihedral-angle-corrected registry-dependent potential, which we couple for non-bonded interaction with either the AIREBO potential or the screened potential ReboScr2. We show that both of these potentials show some shortcomings in the investigation of the mechanical properties of bundles with carbon linkers. [ABSTRACT FROM AUTHOR]

Solving the dendrite growth problem is critical for the development of lithium metal anode for high‐capacity batteries. In this work, a machine learning force field model in combination with a self‐consistent continuum solvation model is used to simulate the morphology evolution of dendrites in a working electrolyte environment. The dynamic evolution of the dendrite morphology can be described in two stages. In the first stage, the energy reduction of the surface atoms induces localized reorientation of the originally single‐crystal dendrite and the formation of multiple domains. In the second stage, the energy reduction of internal atoms drives the migration of grain boundaries and the slipping of crystal domains. The results indicate that the formation of multiple domains might help to stabilize the dendrite, as a higher temperature trajectory in a single crystal dendrite without domains shows a higher dendrite collapsing rate. Several possible modes of morphological evolutions are also investigated, including surface diffusion of adatoms and configuration twists from [100] exposed surfaces to [110] exposed surfaces. In summary, reducing the surface and grain boundary energy drives the morphology evolution. Based on the analysis of these driving forces, some guidelines are suggested for designing a more stable lithium metal anode. [ABSTRACT FROM AUTHOR]

[Display omitted] • Enhanced stability and bio-compatibility of BNNTs make them excellent 1D candidates for drug delivery applications. • DFT and MD offer a new paradigm for identifying the potential of nanocarriers beyond experimental methods. • DFT provides detailed insights into the interaction mechanisms of drug molecules with BNNTs. • MD simulations offer a comprehensive understanding of the dynamic behavior of drug-BNNT complex. Exploring diverse nanocarriers in targeted drug delivery research is vital for optimizing therapeutic efficacy and minimizing adverse effects, thus improving treatment outcomes across various medical conditions. Boron Nitride Nanotubes (BNNTs) have emerged as one of the most promising one-dimensional nanostructures for drug delivery, owing to their unique structural and chemical properties. Herein, the authors explore and review the potential of BNNTs as drug delivery vehicles through the use of computational techniques, including Density Functional Theory (DFT) and Molecular Dynamics (MD) simulations. These simulation techniques yield valuable insights into drug encapsulation, transportation, and release mechanisms through a range of calculations, encompassing adsorption, thermodynamics, electronic characteristics, and chemical interactions. Employing both DFT and MD simulations in drug delivery studies, offers a distinct advantage by providing detailed insights into atomic-level interactions, and exploring dynamic processes from drug loading or encapsulation to drug release, which may be difficult to achieve solely through experimental means. This concise review offers an overview of findings and obstacles delineated in the use of BNNTs, as well as its various analogues for drug delivery. [ABSTRACT FROM AUTHOR]

• The simulation cell describes the physicochemical aspects of the hydrogen production. • The surface of calcite keeps hydrogen strongly bonded and the asphaltene helps to keep them in static equilibrium of forces. • The hydrogen is released from limestone rock surface by the asphaltene- zwitterionic liquids (ZL) system. A theoretical model is presented to describe the processes of release and desorption of molecular hydrogen from the depths of calcite rock oil fields via surfactants such as zwitterionic liquids. This model is based on molecular mechanics and dynamics, for which we built a supercell with a calcite crystalline unit cell in order to model the {1,0,4} surface and volume of the calcite rock; 42 H 2 molecules adsorbed and interacting strongly with the calcite surface stone were analyzed in this work; the effect of crude oil is simulated with an asphaltene macromolecule model. Finally, a branched geminal zwitterionic liquid (ZL) molecule is applied to initiate the process of hydrogen desorption and release. The aqueous medium of the oil field is simulated by the dielectric constant of water. The most stable molecules were obtained using forcite computational code. While the interaction energies were calculated by classical molecular mechanics using Dreiding force field. The dynamics of the H 2 desorption and release process from calcite rock of oil well is studied using the classical molecular dynamics. The purpose of the work is to theoretically demonstrate that ZL substances are capable of releasing and desorb hydrogen from the calcite surface in the depths of oil fields. The results indicate that ZL polar groups form cation-π interactions with the asphaltene benzene rings structure, and that, the carbonated chains of the former trap the latter to release molecular hydrogen. Finally, the aqueous medium desorbs hydrogen from the calcite surface and transports it to the surface of the oil field. [ABSTRACT FROM AUTHOR]

We present a polarisable force field parametrisation for amino acid-based ionic liquids based on the Amoeba framework. The force field has been obtained using accurate ab initio data and has been conceived to be as compatible as possible with the existing Amoeba parametrisation. We present here a validation mainly carried out using reference ab initio calculations and we show how the parametrisation is able to provide the structural features of the fluids to an excellent extent as well as to reproduce the intermolecular interaction energies. [ABSTRACT FROM AUTHOR]

A class of radial, polynomial cutoff functions f c n (r) for short-ranged pair potentials or related expressions is proposed. Their derivatives up to order n and n + 1 vanish at the outer cutoff r c and an inner radius r i , respectively. Moreover, f c n (r ≤ r i ) = 1 and f c n (r ≥ r c ) = 0. It is shown that the used order n can qualitatively affect results: stress and bulk moduli of ideal crystals are unavoidably discontinuous with density for n = 0 and n = 1, respectively. Systematic errors on energies and computing times decrease by 20–50% for Lennard-Jones with n = 2 or n = 3 compared to standard cutting procedures. Another cutoff function turns out beneficial to compute Coulomb interactions using the Wolf summation, which is shown to not properly converge when local charge neutrality is obeyed only in a stochastic sense. However, for all investigated homogeneous systems with thermal noise (ionic crystals and liquids), the modified Wolf summation, despite being infinitely differentiable at r c , converges similarly quickly as the original summation. Finally, it is discussed how to reduce the computational burden of numerically exact Monte Carlo simulations using the Wolf summation even when it does not properly converge. [ABSTRACT FROM AUTHOR]

Machine learning (ML) has been an important arsenal in computational biology used to elucidate protein function for decades. With the recent burgeoning of novel ML methods and applications, new ML approaches have been incorporated into many areas of computational biology dealing with protein function. We examine how ML has been integrated into a wide range of computational models to improve prediction accuracy and gain a better understanding of protein function. The applications discussed are protein structure prediction, protein engineering using sequence modifications to achieve stability and druggability characteristics, molecular docking in terms of protein–ligand binding, including allosteric effects, protein–protein interactions and protein-centric drug discovery. To quantify the mechanisms underlying protein function, a holistic approach that takes structure, flexibility, stability, and dynamics into account is required, as these aspects become inseparable through their interdependence. Another key component of protein function is conformational dynamics, which often manifest as protein kinetics. Computational methods that use ML to generate representative conformational ensembles and quantify differences in conformational ensembles important for function are included in this review. Future opportunities are highlighted for each of these topics. [ABSTRACT FROM AUTHOR]

In this research, a combined study on structures and vibrational spectra of antiviral rimantadine have been performed using hybrid B3LYP/6–311++G∗∗ calculations and the scaled quantum force field (SQMFF) procedure. Harmonic force fields and scaled force constants of Free Base (FB), Cationic (CA) and Hydrochloride (HCl) species derived from the antiviral rimantadine have been calculated in gas phase and in aqueous solution using normal internal coordinates and scaling factors. Good correlations were acquired comparing the theoretical IR, Raman, 1H– 13C-NMR and UV spectra of three species with the analogous experimental ones, suggesting probably, the presence of all them in both phases. The main force constants of three species have evidenced lower values than the corresponding to antiviral amantadine. The ionic character of N1–H33⋯Cl36 bond of HCl species in aqueous solution evidence positive Mulliken charge on N1 atom indicating that this species is as CA one. Rimantadine presents higher solvation energies in water than other antiviral species, such as chloroquin, niclosamide, cidofovir and brincidofovir. The FB and HCl species of rimantadine are slightly less reactive than the corresponding to amantadine while the opposite is observed for the CA species. The predicted ECD spectra for the FB and CA species show positive Cotton effect different from the negative observed for the HCl one. These different behaviours of three species of rimantadine could probably explain the differences observed in the intensities of bands predicted in the electronic spectra of these species.

[Display omitted] • Complete vibrational assignments of all species and its harmonic scaled force constants are reported. • NBO and AIM calculations support the higher stability of monohydrate dimer due to the six H bonds interactions. • The monohydrated dimer reveals a higher solvation energy in aqueous solution. • The monohydrate dimer is the most reactive species, as revealed by the lowest gap value. • Anhydrous and monohydrate species evidence by NBO calculations a very important delocalization of electrons. Experimental FT-IR, FT-Raman and UV spectra have been combined with hybrid B3LYP/6–311++G** calculations and the scaled quantum mechanical force field (SQMFF) methodology to study structural and vibrational properties of monohydrated dimer (MD) of violuric acid in gas and aqueous media. Complete vibrational assignments and its scaled force constants are reported together with the corresponding to anhydrous and monohydrate monomer. From four anhydrous C1, C2, C3, C4 monomers, C4 is the most stable in both media. In solution, the initial structure of C2 change to the tautomeric species most stable C4. The MD reveals a higher solvation energy while natural bond orbital (NBO) and atoms in molecules (AIM) calculations support the higher stability of this species due to the six H bonds interactions and to its higher expansion of volume in solution. The MD is the most reactive species, as revealed by the lowest gap value and by high global electrophilicity and most negative global nucleophilicity indexes. Very good concordances are observed among the predicted IR, Raman, 13C NMR and UV spectra and the corresponding experimental ones. Comparisons of predicted 13C NMR and electronic spectra with the experimental one show that those three species of violuric acid could be present in aqueous solution. Similar f(νC=O) and f(νC-O) force constants for the three species are justified by the important delocalization of electrons evidenced in anhydrous and monohydrate species by NBO calculations. [ABSTRACT FROM AUTHOR]

Dipalmitoylphosphatidylcholine is a phospholipid of major importance for biological systems. Molecular dynamics simulation investigations of this lipid focused on their behavior at human body temperature (≈ 37 °C or 310 K). For some applications, however, it is necessary to study its properties at room temperature (≈ 25 °C or 298 K). A small difference in temperature at this range is responsible for a phase transition in the lipid bilayer. Therefore, molecular dynamics simulations are carried out applying six different force fields from the GROMOS family to compare the less ordered phase (liquid-crystalline phase) with a more ordered phase (gel phase) of the hydrated bilayer of dipalmitoylphosphatidylcholine formed at 323 K and 298 K, respectively. The analysis of the bilayer structural quantities is used to evaluate order and packing at the two temperatures. The area per lipid and deuterium order parameter results are in agreement with simulation results from the literature and are also compared with experimental data. These parameters, however, do not provide a clear picture of the low-temperature gel phase present in the simulations using the force fields from the GROMOS family. To address this, a strategy for computing the lipid phase nematic order parameter is proposed, and a reference gel phase is simulated using the CHARMM36 force field for comparison. The results are consistent with other structural quantities but reveal a level of order that is below the expected for an ordered gel phase for the GROMOS force fields. Visual inspection of the simulation trajectories suggests the presence of a ripple phase. Overall, the GROMOS 54A8 provides the best performance among the tested GROMOS force fields for both investigated temperatures, even lacking a proper representation of the ordered gel phase. [Display omitted] • Among the GROMOS family, 54A8 is the one that provides the best agreement with the APL and deuterium order parameter experimental data for DPPC bilayer. • None of the force fields from GROMOS family reproduce the structure expected for DPPC bilayers at 298 K. • The nematic order parameter seems to be a more adequate quantity to evaluate lipid bilayer phase. [ABSTRACT FROM AUTHOR]

We introduce TUPÃ, a Python‐based algorithm to calculate and analyze electric fields in molecular simulations. To demonstrate the features in TUPÃ, we present three test cases in which the orientation and magnitude of the electric field exerted by biomolecules help explain biological phenomena or observed kinetics. As part of TUPÃ, we also provide a PyMOL plugin to help researchers visualize how electric fields are organized within the simulation system. The code is freely available and can be obtained at https://mdpoleto.github.io/tupa/. [ABSTRACT FROM AUTHOR]

Most decision-supports methods are dedicated to the identification and characterization of risks and opportunities. The concrete exploitation of these risks and opportunities is generally depending on the ability of users to analyze multi-dimensional situations, to mobilize their experience and to foresee consequences. In this article, a new and original data science-based vision of risk and opportunity management for decision-making purpose is introduced. The main expected benefit of this vision is to enable decision makers to manage the performance trajectory of a considered system by visualizing and combining the impact of risks and opportunity. [ABSTRACT FROM AUTHOR]

In this article, two main approaches to the prediction of thermal conductivities by molecular dynamics (MD) simulations are discussed, namely non-equilibrium molecular dynamics simulations (NEMD) and the application of the Green–Kubo formula, i.e., EMD. NEMD methods are more affected by size effects than EMD methods. The thermal conductivities of silicone rubbers in special were found as a function of the degree of crosslinking. Moreover, the thermal conductivities of thermoplastic polyurethane as function of the mass fraction of soft segments were obtained by those MD simulations. All results are in good agreement with data from the experimental literature. After the analysis of normalized heat flux autocorrelation functions, it has been revealed that heat in the polymers is mainly transferred by low-frequency phonons. Simulation details as well as advantages and disadvantages of the single methods are discussed in the article. [ABSTRACT FROM AUTHOR]

Carbon force fields are widely used for obtaining structural properties of carbon nanomaterials. We evaluate the performance of a wide range of carbon force fields for obtaining molecular structures of prototypical C60 fullerenes. The reference geometries are optimized using the hybrid B3LYP-D3BJ density functional. The Gaussian approximation potential (GAP-20) machine-learning-based force field attains a root-mean-square deviation (RMSD) of merely 0.014 Å over a set of 29 unique C–C bond distances. This represents a significant improvement over traditional empirical force fields, which result in RMSDs ranging between 0.023 (LCBOP-I) and 0.073 (EDIP) Å. Performance of the GAP-20 force field is on par with that of the PM6 and AM1 semiempirical methods. Moreover, the GAP-20 force field attains a mean signed deviation of 0.003 Å indicating it is free of systematic bias toward underestimating or overestimating the fullerene bond distances. We therefore recommend the GAP-20 force field for optimizing the equilibrium structures of fullerenes and nanotubes. [ABSTRACT FROM AUTHOR]

Atomic-level understanding of the dynamic feature of host–guest interactions remains a central challenge in supramolecular chemistry. The remarkable guest binding behavior of the Cucurbiturils family of supramolecular containers makes them promising drug carriers. Among Cucurbit[n]urils, Cucurbit[8]uril (CB8) has an intermediate portal size and cavity volume. It can exploit almost all host–guest recognition motifs formed by this host family. In our previous work, an extensive computational investigation of the binding of seven commonly abused and structurally diverse drugs to the CB8 host was performed, and a general dynamic binding picture of CB8-guest interactions was obtained. Further, two widely used fixed-charge models for drug-like molecules were investigated and compared in great detail, aiming at providing guidelines in choosing an appropriate charge scheme in host-guest modelling. Iterative refitting of atomic charges leads to improved binding thermodynamics and the best root-mean-squared deviation from the experimental reference is 2.6 kcal/mol. In this work, we focus on a thorough evaluation of the remaining parts of classical force fields, i.e., the bonded interactions. The widely used general Amber force fields are assessed and refitted with generalized force-matching to improve the intra-molecular conformational preference, and thus the description of inter-molecular host–guest interactions. The interaction pattern and binding thermodynamics show a significant dependence on the modelling parameters. The refitted system-specific parameter set improves the consistency of the modelling results and the experimental reference significantly. Finally, combining the previous charge-scheme comparison and the current force-field refitting, we provide general guidelines for the theoretical modelling of host–guest binding.

The study of tau protein aggregation and interactions with other molecules or solvents using molecular dynamics simulations (MDs) is of interest to many researchers to propose new mechanism-based therapeutics for neurodegenerative diseases such as Alzheimer’s disease, Pick’s disease, chronic traumatic encephalopathy, and other tauopathies. In this review, we present recent MD simulation studies of tau oligomers and fibrils such as tau-NPK, tau-PHF, tau-K18, and tau-R3-R4 monomers and dimers. All-atom simulations by replica exchange MDs and coarse-grained MDs in lipid bilayers and in solution were used. The simulations revealed different mechanisms in the binding of tau in bilayers and in solutions, depending on the peptide size. Phosphorylation is also an important factor in MD simulations. The use of steered MDs was also included to simulate the dissociation of tau fibrils. The exponential improvement in the computing power of computers has led to an increasing number of scientists and engineers using a cost-effective, high-performance computing platform to study how the tau protein interacts and the effects of changing its structure, such as the phosphorylation of tau fibrils.

Machine learning (ML) using data sets of atomic and molecular force fields (FFs) has made significant progress and provided benefits in the fields of chemistry and material science. This work examines the interactions between chemistry and materials computational science at the atomic and molecular scales for metal-organic framework (MOF) adsorbent development toward carbon dioxide (CO 2 ) capture. Herein, a connection will be drawn between atomic forces predicted by ML algorithms and the structures of MOFs for CO 2 adsorption. Our study also takes into account the successes of atomic computational screening in the field of materials science, especially quantum ML, and its relationship to ML algorithms that clarify advancements in the area of CO 2 adsorption by MOFs. Additionally, we reviewed the processes for supplying data to ML algorithms for algorithm training, including text mining from scientific articles, and MOF's formula processing linked to the chemical properties of MOFs. To create ML algorithms for future research, we recommend that the digitization of scientific records can help efficiently synthesize advanced MOFs. Finally, a future vision for developing pioneer MOF synthesis routes for CO 2 capture is presented in this review article.