Modification of MM force fields around heme-Fe in the CYP-ligand complex and ab initio FMO calculations for the complex.

Cytochrome P450 (CYP) enzymes play essential roles in the synthesis and metabolic activation of physiologically active substances. CYP has a prosthetic heme (iron protoporphyrin IX) in its active center, where Fe ion (heme-Fe) is deeply involved in enzymatic reactions of CYP. To precisely describe the structure and electronic states around heme-Fe, we modified the force fields (FFs) around heme-Fe in molecular mechanics (MM) simulations and conducted ab initio fragment molecular orbital (FMO) calculations for the CYP-ligand complex. To describe the coordination bond between heme-Fe and its coordinated ligand (ketoconazole), we added FF between heme-Fe and the N atom of ketoconazole, and then the structure of the complex was optimized using the modified FF. Its adequacy was confirmed by comparing the MM-optimized structure with the X-ray crystal one of the CYP-ketoconazole complex. We also performed 100 ns molecular dynamics simulations and revealed that the coordination bonds around heme-Fe were maintained even at 310 K and that the CYP-ketoconazole structure was kept similar to the X-ray structure. Furthermore, we investigated the electronic states of the complex using the ab initio FMO method to identify the CYP residues and parts of ketoconazole that contribute to strong binding between CYP and ketoconazole. The present procedure of constructing FF between heme-Fe and ketoconazole can be applicable to other CYP-ligand complexes, and the modified FF can provide their accurate structures useful for predicting the specific interactions between CYP and its ligands.

Structure, Properties, and Applications of Silica Nanoparticles: Recent Theoretical Modeling Advances, Challenges, and Future Directions.

Silica nanoparticles (SNPs), one of the most widely researched materials in modern science, are now commonly exploited in surface coatings, biomedicine, catalysis, and engineering of novel self-assembling materials. Theoretical approaches are invaluable to enhancing fundamental understanding of SNP properties and behavior. Tremendous research attention is dedicated to modeling silica structure, the silica-water interface, and functionalization of silica surfaces for tailored applications. In this review, the range of theoretical methodologies are discussed that have been employed to model bare silica and functionalized silica. The evolution of silica modeling approaches is detailed, including classical, quantum mechanical, and hybrid methods and highlight in particular the last decade of theoretical simulation advances. It is started with discussing investigations of bare silica systems, focusing on the fundamental interactions at the silica-water interface, following with a comprehensively review of the modeling studies that examine the interaction of silica with functional ligands, peptides, ions, surfactants, polymers, and carbonaceous species. The review is concluded with the perspective on existing challenges in the field and promising future directions that will further enhance the utility and importance of the theoretical approaches in guiding the rational design of SNPs for applications in engineering and biomedicine.

Drude2019IDPC polarizable force field reveals structure-function relationship of insulin.

Intrinsically disordered proteins (IDPs) lack stable tertiary structures under physiological conditions, yet play key roles in biological processes and associated with human complex diseases. Their conformational characteristics and high content of charged residues make the use of polarizable force fields an advantageous for simulating IDPs. The Drude2019IDP polarizable force field, previously introduced, has demonstrated comprehensive enhancements and improvements in dipeptides, short peptides, and IDPs, achieving a balanced sampling between IDPs and structured proteins. However, the performance in simulating 5 dipeptides was found to be underestimate. Therefore, we individually performed reweighting and grid-based energy correction map (CMAP) optimization for these 5 dipeptides, resulting in the enhanced Drude2019IDPC force field. The performance of Drude2019IDPC was evaluated with 5 dipeptides, 5 disordered short peptides, and a representative IDP. The results demonstrated a marked improvement comparing with original Drude2019IDP. To further substantiate the capabilities of Drude2019IDPC, MD simulation and Markov state model (MSM) were applied to wild type and mutant for insulin, to elucidate the difference of conformational characteristics and transition path. The findings reveal that mutation can maintain the monomorphic characteristics, providing insights for engineered insulin development. These results indicate that Drude2019IDPC could be used to reveal the structure-function relationship for other proteins.

GPTFF: A high-accuracy out-of-the-box universal AI force field for arbitrary inorganic materials.

This study introduces a novel artificial intelligence (AI) force field, namely a graph-based pre-trained transformer force field (GPTFF), which can simulate arbitrary inorganic systems with good precision and generalizability. Harnessing a large trove of the data and the attention mechanism of transformer algorithms, the model can accurately predict energy, atomic force, and stress with mean absolute error (MAE) values of 32 meV/atom, 71 meV/Å, and 0.365 GPa, respectively. The dataset used to train the model includes 37.8 million single-point energies, 11.7 billion force pairs, and 340.2 million stresses. We also demonstrated that the GPTFF can be universally used to simulate various physical systems, such as crystal structure optimization, phase transition simulations, and mass transport. The model is publicly released with this paper, enabling anyone to use it immediately without needing to train it.

Effect of Phasic Experimental Pain Applied during Motor Preparation or Execution on Motor Performance and Adaptation in a Reaching Task: A Randomized Trial.

Musculoskeletal conditions often involve pain related to specific movements. However, most studies on the impact of experimental pain on motor performance and learning have used tonic pain models. This study aimed to evaluate the effect of experimental phasic pain during the preparation or execution of a reaching task on the acquisition and retention of sensorimotor adaptation. Participants were divided into three groups: no pain, pain during motor preparation, and pain during motor execution. Pain was induced over the scapula with a laser while participants performed a force field adaptation task over two days. To assess the effect of pain on motor performance, two baseline conditions (with or without pain) involving unperturbed pointing movements were also conducted. The results indicated that the timing of the nociceptive stimulus differently affected baseline movement performance. Pain during motor preparation shortened reaction time, while pain during movement execution decreased task performance. However, when these baseline effects were accounted for, no impact of pain on motor adaptation or retention was observed. All groups showed significant improvements in all motor variables for both adaptation and retention. In conclusion, while acute phasic pain during motor preparation or execution can affect the movement itself, it does not interfere with motor acquisition or retention during a motor adaptation task.

Exploiting Metal-Organic Frameworks for Vinylidene Fluoride Adsorption: From Force Field Development, Computational Screening to Machine Learning.

Metal-organic frameworks (MOFs) represent a distinctive class of nanoporous materials with considerable potential across a wide range of applications. Recently, a handful of MOFs has been explored for the storage of environmentally hazardous fluorinated gases (Keasler et al. Science 2023, 381, 1455), yet the potential of over 100,000 MOFs for this specific application has not been thoroughly investigated, particularly due to the absence of an established force field. In this study, we develop an accurate force field for nonaversive hydrofluorocarbon vinylidene fluoride (VDF) and conduct high-throughput computational screening to identify top-performing MOFs with high VDF adsorption capacities. Quantitative structure-property relationships are analyzed via machine learning models on the combinations of geometric, chemical, and topological features, followed by feature importance analysis to probe the effects of these features on VDF adsorption. Finally, from detailed structural analysis via radial distribution functions and spatial densities, we elucidate the significance of different interaction modes between VDF and metal nodes in top-performing MOFs. By synergizing force-field development, computational screening, and machine learning, our findings provide microscopic insights into VDF adsorption in MOFs that will advance the development of new nanoporous materials for high-performance VDF storage or capture.

Methods for Classical-Mechanical Molecular Simulation in Chemistry: Achievements, Limitations, Perspectives.

More than a half century ago it became feasible to simulate, using classical-mechanical equations of motion, the dynamics of molecular systems on a computer. Since then classical-physical molecular simulation has become an integral part of chemical research. It is widely applied in a variety of branches of chemistry and has significantly contributed to the development of chemical knowledge. It offers understanding and interpretation of experimental results, semiquantitative predictions for measurable and nonmeasurable properties of substances, and allows the calculation of properties of molecular systems under conditions that are experimentally inaccessible. Yet, molecular simulation is built on a number of assumptions, approximations, and simplifications which limit its range of applicability and its accuracy. These concern the potential-energy function used, adequate sampling of the vast statistical-mechanical configurational space of a molecular system and the methods used to compute particular properties of chemical systems from statistical-mechanical ensembles. During the past half century various methodological ideas to improve the efficiency and accuracy of classical-physical molecular simulation have been proposed, investigated, evaluated, implemented in general simulation software or were abandoned. The latter because of fundamental flaws or, while being physically sound, computational inefficiency. Some of these methodological ideas are briefly reviewed and the most effective methods are highlighted. Limitations of classical-physical simulation are discussed and perspectives are sketched.

Designing Accurate Moment Tensor Potentials for Phonon-Related Properties of Crystalline Polymers.

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.

Ion Transport at Polymer-Argyrodite Interfaces.

Solid-state electrolytes, particularly polymer/ceramic composite electrolytes, are emerging as promising candidates for lithium-ion batteries due to their high ionic conductivity and mechanical flexibility. The interfaces that arise between the inorganic and organic materials in these composites play a crucial role in ion transport mechanisms. While lithium ions are proposed to diffuse across or parallel to the interface, few studies have directly examined the quantitative impact of these pathways on ion transport and little is known about how they affect the overall conductivity. Here, we present an atomistic study of lithium-ion (Li+) transport across well-defined polymer-argyrodite interfaces. We present a force field for polymer-argyrodite interfacial systems, and we carry out molecular dynamics and enhanced sampling simulations of several composite systems, including poly(ethylene oxide) (PEO)/Li6PS5Cl, hydrogenated nitrile butadiene rubber (HNBR)/Li6PS5Cl, and poly(vinylidene fluoride-co-hexafluoropropylene) (PVDF-HFP)/Li6PS5Cl. For the materials considered here, Li-ion exhibits a preference for the ceramic material, as revealed by free energy differences for Li-ion between the inorganic and the organic polymer phase in excess of 13 kBT. The relative free energy profiles of Li-ion for different polymeric materials exhibit similar shapes, but their magnitude depends on the strength of interaction between the polymers and Li-ion: the greater the interaction between the polymer and Li-ions, the smaller the free energy difference between the inorganic and organic materials. The influence of the interface is felt over a range of approximately 1.5 nm, after which the behavior of Li-ion in the polymer is comparable to that in the bulk. Near the interface, Li-ion transport primarily occurs parallel to the interfacial plane, and ion mobility is considerably slower near the interface itself, consistent with the reduced segmental mobility of the polymer in the vicinity of the ceramic material. These findings provide insights into ionic complexation and transport mechanisms in composite systems, and will help improve design of improved solid electrolyte systems.

Durability of motor learning by observing.

Information about another person's movement kinematics obtained through visual observation activates brain regions involved in motor learning. Observation-related changes in these brain areas are associated with adaptive changes to feedforward neural control of muscle activation and behavioral improvements in limb movement control. However, little is known about the stability of these observation-related effects over time. Here, we used force channel trials to probe changes in lateral force production at various time points (1 min, 10 min, 30 min, 60 min, 24 h) after participants either physically performed, or observed another individual performing upper limb reaching movements that were perturbed by novel, robot-generated forces (a velocity-dependent force-field). Observers learned to predictively generate directionally and temporally specific compensatory forces during reaching, consistent with the idea that they acquired an internal representation of the novel dynamics. Participants who physically practiced in the force-field showed adaptation that was detectable at all time points, with some decay detected after 24 h. Observation-related adaptation was less temporally stable in comparison, decaying slightly after 1 h and undetectable at 24 h. Observation induced less adaptation overall than physical practice, which could explain differences in temporal stability. Visually acquired representations of movement dynamics are retained and continue to influence behavior for at least 1 h after observation.NEW & NOTEWORTHY We used force channel probes in an upper limb force-field reaching task in humans to compare the durability of learning-related changes that occurred through visual observation to those after physical movement practice. Visually acquired representations of movement dynamics continued to influence behavior for at least 1 h after observation. Our findings point to a 1-h window during which visual observation of another person could play a role in motor learning.

A Polarizable Forcefields for Glyoxal Acetals as Electrolyte Components for Lithium-Ion Batteries.

In this work we have derived the parameters of an AMOEBA-like polarizable forcefield for electrolytes based on tetramethoxy and tetraethoxy-glyoxal acetals, and propylene carbonate. The resulting forcefield has been validated using both ab-initio data and the experimental properties of the fluids. Using molecular dynamics simulations, we have investigated the structural features and the solvation properties of both the neat liquids and of the corresponding 1 M LiTFSI electrolytes at the molecular level. We present a detailed analysis of the Li ion solvation shells, of their structure and highlight the different behavior of the solvents in terms of their molecular structure and coordinating features.

An automated calculation pipeline for differential pair interaction energies with molecular force fields using the Tinker Molecular Modeling Package.

An automated pipeline for comprehensive calculation of intermolecular interaction energies based on molecular force-fields using the Tinker molecular modelling package is presented. Starting with non-optimized chemically intuitive monomer structures, the pipeline allows the approximation of global minimum energy monomers and dimers, configuration sampling for various monomer-monomer distances, estimation of coordination numbers by molecular dynamics simulations, and the evaluation of differential pair interaction energies. The latter are used to derive Flory-Huggins parameters and isotropic particle-particle repulsions for Dissipative Particle Dynamics (DPD). The computational results for force fields MM3, MMFF94, OPLS-AA and AMOEBA09 are analyzed with Density Functional Theory (DFT) calculations and DPD simulations for a mixture of the non-ionic polyoxyethylene alkyl ether surfactant C10E4 with water to demonstrate the usefulness of the approach.Scientific ContributionTo our knowledge, there is currently no open computational pipeline for differential pair interaction energies at all. This work aims to contribute an (at least academically available, open) approach based on molecular force fields that provides a robust and efficient computational scheme for their automated calculation for small to medium-sized (organic) molecular dimers. The usefulness of the proposed new calculation scheme is demonstrated for the generation of mesoscopic particles with their mutual repulsive interactions.

On-the-Fly Machine Learning Force Field Study of Liquid-Al/Solid-TiB2 Interfaces.

Using the on-the-fly machine learning force field, simulations were performed to study the atomic structure evolution of the liquid-Al/solid-TiB2 interface with two different terminations, aiming to deepen the understanding of the mechanism of TiB2 as nucleating particles in an aluminum alloy. We conducted simulations using MLFF for up to 100 ps, enabling us to observe the interfacial properties from a deeper and more comprehensive perspective. The nucleation potential of TiB2 particles is determined by the formation of various ordered structures at the interface, which is significantly influenced by the termination of the TiB2 (0001) surface. The evolution of the interface during heterogeneous nucleation processes with different terminations is described using structural information and dynamic characteristics. The Ti-terminated surface is more prone to forming quasi-solid regions compared to the B-termination. Analysis of mean square displacement and vibrational density of states indicates that the liquid layer at the Ti-terminated interface is closer in characteristics to a solid compared to the B-terminated interface. We also found that on the TiB2 (0001) surface different terminations give rise to distinct ordered structures at the interfaces, which is ascribed to their different diffusion abilities.

Dimerization of Full-length Aß -42 Peptide: A Comparison of Different Force Fields and Water Models.

Among the two isoforms of amyloid- i.e., Aß-40 and Aß-42, Aß-42 is more toxic due to its increased aggregation propensity. The oligomerization pathways of amyloid-ß may be investigated by studying its dimerization process at an atomic level. Intrinsically disordered proteins (IDPs) lack well-defined structures and are associated with numerous neurodegenerative disorders. Molecular dynamics simulations of these proteins are often limited by the choice of parameters due to inconsistencies in the empirically developed protein force fields and water models. To evaluate the accuracy of recently developed force fields for IDPs, we study the dimerization of full-length Aß-42 in aqueous solution with three different combinations of AMBER force field parameters and water models such as ff14SB/TIP3P, ff19SB/OPC, and ff19SB/TIP3P using classical MD and Umbrella Sampling method. This work may be used as a benchmark to compare the performance of different force fields for the simulations of IDPs.

Molecular dynamics simulation of salt diffusion in constituting phosphazene-based polymer electrolyte.

A growing demand to visualize polymer models in liquid poses a computational challenge in molecular dynamics (MD) simulation, as this requires emerging models under suitable force fields (FFs) to capture the underlying molecular behaviour accurately. In our present study, we have employed TIP3P potential on water and all atomistic optimized potentials for liquid simulations FFs to study the liquid electrolyte behavior of phosphazene-based polymer by considering its potential use in lithium-ion polymer batteries. We have explored the polymer's local structure, chain packing, wettability, and hydrophobic tendencies against the silicon surface using a combination of a pseudocontinuum model in MD simulation, and surface-sensitive sum frequency generation (SFG) vibrational spectroscopy. The finding yields invaluable insights into the molecular architecture of phosphazene. This approach identifies the importance of hydrophobic interactions with air and hydrophilic units with water molecules in understanding the behavior and properties of phosphazene-based polymers at interfaces, contributing to its advancements in materials science. The MD study uniquely captures traces of the polymer-ion linkage, which is observed to become more pronounced with the increase in polymer weight fraction. The theoretical observation of this linkage's influence on lithium-ion diffusion motion offers valuable insights into the fundamental physics governing the behavior of atoms and molecules within phosphazene-based polymer electrolytes in aqueous environments. Further these predictions are corroborated in the molecular-level depiction at the air-aqueous interface, as evidenced from the OH-oscillator strength variation measured by the SFG spectroscopy.The fundamental findings from this study open new avenues for utilizing MD simulation as a versatile methodology to gain profound insights into intermolecular interactions of polymer. It could be useful in the application of biomedical and energy-related research, such as polymer lithium-ion batteries, fuel cells, and organic solar cells.

Thermomechanical Properties of Transition Metal Dichalcogenides Predicted by a Machine Learning Parameterized Force Field.

The mechanical and thermal properties of transition metal dichalcogenides (TMDs) are directly relevant to their applications in electronics, thermoelectric devices, and heat management systems. In this study, we use a machine learning (ML) approach to parametrize molecular dynamics (MD) force fields to predict the mechanical and thermal transport properties of a library of monolayered TMDs (MoS2, MoTe2, WSe2, WS2, and ReS2). The ML-trained force fields were then employed in equilibrium MD simulations to calculate the lattice thermal conductivities of the foregoing TMDs and to investigate how they are affected by small and large mechanical strains. Furthermore, using nonequilibrium MD, we studied thermal transport across grain boundaries. The presented approach provides a fast albeit accurate methodology to compute both mechanical and thermal properties of TMDs, especially for relatively large systems and spatially complex structures, where density functional theory computational cost is prohibitive.

Analyzing lipid distributions and curvature in molecular dynamics simulations of complex membranes.

We describe methods to analyze lipid distributions and curvature in membranes with complex lipid mixtures and embedded membrane proteins. We discuss issues involved in these analyses, available tools to calculate curvature preferences of lipids and proteins, and focus on tools developed in our group for visual analysis of lipid-protein interactions and the analysis of membrane curvature.

Atomistic Removal Mechanisms of SiC in Hydrogen Peroxide Solution.

To elucidate the atomic mechanisms of the chemical mechanical polishing (CMP) of silicon carbide (SiC), molecular dynamics simulations based on a reactive force field were used to study the sliding process of silica (SiO2) abrasive particles on SiC substrates in an aqueous H2O2 solution. During the CMP process, the formation of Si-O-Si interfacial bridge bonds and the insertion of O atoms at the surface can lead to the breakage of Si-C bonds and even the complete removal of SiC atoms. Furthermore, the removal of C atoms is more difficult than the removal of Si atoms. It is found that the removal of Si atoms largely influences the removal of C atoms. The removal of Si atoms can destroy the lattice structure of the substrate surface, leading the neighboring C atoms to be bumped or even completely removed. Our research shows that the material removal during SiC CMP is a comprehensive result of different atomic-level removal mechanisms, where the formation of Si-O-Si interfacial bridge bonds is widespread throughout the SiC polishing process. The Si-O-Si interfacial bridge bonds are the main removal mechanisms for SiC atoms. This study provides a new idea for improving the SiC removal process and studying the mechanism during CMP.

Sodium Triflate Water-in-Salt Electrolytes in Advanced Battery Applications: A First-Principles-Based Molecular Dynamics Study.

Offering a compelling combination of safety and cost-effectiveness, water-in-salt (WiS) electrolytes have emerged as promising frontiers in energy storage technology. Still, there is a strong demand for research and development efforts to make these electrolytes ripe for commercialization. Here, we present a first-principles-based molecular dynamics (MD) study addressing in detail the properties of a sodium triflate WiS electrolyte for Na-ion batteries. We have developed a workflow based on a machine learning (ML) potential derived from ab initio MD simulations. As ML potentials are typically restricted to the interpolation of the data points of the training set and have hardly any predictive properties, we subsequently optimize a classical force field based on physics principles to ensure broad applicability and high performance. Performing and analyzing detailed MD simulations, we identify several very promising properties of the sodium triflate as a WiS electrolyte but also indicate some potential stability challenges associated with its use as a battery electrolyte.

Force-field perturbations and muscle vibration strengthen stability-related foot placement responses during steady-state gait in healthy adults.

Mediolateral gait stability can be maintained by coordinating our foot placement with respect to the center-of-mass (CoM) kinematic state. Neurological impairments can reduce the degree of foot placement control. For individuals with such impairments, interventions that could improve foot placement control could thus contribute to improved gait stability. In this study we aimed to better understand two potential interventions, by investigating their effect in neurologically intact individuals. The degree of foot placement control can be quantified based on a foot placement model, in which the CoM position and velocity during swing predict subsequent foot placement. Previously, perturbing foot placement with a force-field resulted in an enhanced degree of foot placement control as an after-effect. Moreover, timed muscle vibration enhanced the degree of foot placement control whilst the vibration was applied. Here, we replicated these two findings and further investigated whether Q1) timed muscle vibration leads to an after-effect and Q2) whether combining timed muscle vibration with force-field perturbations leads to a larger after-effect, as compared to force-field perturbations only. In addition, we evaluated several potential contributors to the degree of foot placement control, by considering foot placement errors, CoM variability and the CoM position gain (ßpos) of the foot placement model, next to the R2 measure as the degree of foot placement control. Timed muscle vibration led to a higher degree of foot placement control as an after-effect (Q1). However, combining timed muscle vibration and force-field perturbations did not lead to a larger after-effect, as compared to following force-field perturbations only (Q2). Furthermore, we showed that the improved degree of foot placement control following force-field perturbations and during/following muscle vibration, did not reflect diminished foot placement errors. Rather, participants demonstrated a stronger active response (higher ßpos) as well as higher CoM variability.