Simulation Time Scale Research Articles

Assessing Nonadiabatic Dynamics Methods in Long Timescales.

Nonadiabatic dynamics simulations complement time-resolved experiments by revealing ultrafast excited-state mechanistic information in photochemical reactions. Understanding the relaxation mechanisms of photoexcited molecules finds application in energy, material, and medicinal research. However, with substantial computational costs, the nonadiabatic dynamics simulations have been restricted to ultrafast timescales, typically less than a few picoseconds, thus neglecting a wide range of photoactivated processes occurring in much longer timescales. Before developing new methodologies, we must ask: How well do the popular nonadiabatic dynamics methods perform in a long timescale simulation? In this study, we employ the multiconfiguration time-dependent Hartree (MCTDH) and its multilayer variants (ML-MCTDH), ab initio multiple spawning (AIMS), and fewest-switches surface hopping (FSSH) methodologies to simulate the excited-states dynamics of a weakly coupled multidimensional Spin-Boson model Hamiltonian designed for a long timescale decay behavior. Our study assures that despite having very different theoretical backgrounds, all the above methods deliver qualitatively similar results. While quantum dynamics would be very costly for long timescale simulations, the trajectory-based approaches are paving the way for future advancements.

Leveraging a Separation of States Method for Relative Binding Free Energy Calculations in Systems with Trapped Waters.

Methods for calculating the relative binding free energy (RBFE) between ligands to a target protein are gaining importance in the structure-based drug discovery domain, especially as methodological advances and automation improve accuracy and ease of use. In an RBFE calculation, the difference between the binding affinities of two ligands to a protein is calculated by transforming one ligand into another, in the protein-ligand complex, and in solvent. Alchemical binding free energy calculations are often used for such ligand transformations. Such calculations are not without challenges, however; for example, it can be challenging to handle interfacial waters when these play a crucial role in mediating protein-ligand binding. In some cases, the exchange of the interfacial waters with solvent water might be very infrequent in the course of typical molecular simulations, and such interfacial waters can be considered trapped on the simulation time scale. In these cases, RBFE calculation between two ligands, where one ligand binds with a trapped water while the other ligand displaces it, can result in inaccuracies if the surrounding water structure is not sampled adequately for both ligands. So far, a popular choice for treating the trapped waters in RBFE calculations is to combine free energy calculations with enhanced sampling methods that insert/delete waters in the binding site. Despite recent developments in the enhanced sampling methods, they can result in hysteresis in the RBFE estimate, depending on whether the simulations were started with or without the trapped waters. In this study, we introduce an alternative method, separation of states, to calculate the RBFE between ligand pairs where the ligands bind to the protein with different numbers/positions of trapped waters. The separation of states approach treats the sampling of the trapped waters separately from the free energy calculation of the ligand transformation. In our method, a trapped water in protein's binding site is decoupled from the system first, and the cavity created by its decoupling is stabilized. We then grow a larger ligand into this cavity- a ligand that is known to displace the trapped water. In this study, we show that our method results in precise and accurate estimates of RBFEs for ligand pairs involving the rearrangement of trapped water via RBFE calculations for five such ligand pairs. We have optimized our simulation protocol to be suited for large distributed computational resources and have automated our RBFE calculation workflow.

Tutorial on Molecular Latent Space Simulators (LSSs): Spatially and Temporally Continuous Data-Driven Surrogate Dynamical Models of Molecular Systems.

The inherently serial nature and requirement for short integration time steps in the numerical integration of molecular dynamics (MD) calculations place strong limitations on the accessible simulation time scales and statistical uncertainties in sampling slowly relaxing dynamical modes and rare events. Molecular latent space simulators (LSSs) are a data-driven approach to learning a surrogate dynamical model of the molecular system from modest MD training trajectories that can generate synthetic trajectories at a fraction of the computational cost. The training data may comprise single long trajectories or multiple short, discontinuous trajectories collected over, for example, distributed computing resources. Provided the training data provide sufficient sampling of the relevant thermodynamic states and dynamical transitions to robustly learn the underlying microscopic propagator, an LSS furnishes a global model of the dynamics capable of producing temporally and spatially continuous molecular trajectories. Trained LSS models have produced simulation trajectories at up to 6 orders of magnitude lower cost than standard MD to enable dense sampling of molecular phase space and large reduction of the statistical errors in structural, thermodynamic, and kinetic observables. The LSS employs three deep learning architectures to solve three independent learning problems over the training data: (i) an encoding of the high-dimensional MD into a low-dimensional slow latent space using state-free reversible VAMPnets (SRVs), (ii) a propagator of the microscopic dynamics within the low-dimensional latent space using mixture density networks (MDNs), and (iii) a generative decoding of the low-dimensional latent coordinates back to the original high-dimensional molecular configuration space using conditional Wasserstein generative adversarial networks (cWGANs) or denoising diffusion probability models (DDPMs). In this software tutorial, we introduce the mathematical and numerical background and theory of LSS and present example applications of a user-friendly Python package software implementation to alanine dipeptide and a 28-residue beta-beta-alpha (BBA) protein within simple Google Colab notebooks.

Bridging length and time scales in predictive simulations of thermo-mechanical processes

Abstract This work introduces a theoretical formulation and develops numerical methods for finite element implementation of the formulation so as to extend the concurrent atomistic-continuum (CAC) method for modeling and simulation of finite-temperature materials processes. With significantly reduced degrees of freedom, the CAC simulations are shown to reproduce the results of atomically resolved molecular dynamics simulations for phonon density of states, velocity distributions, equilibrium temperature field of the underlying atomistic model, and also the density, type, and structure of dislocations formed during the kinetic processes of heteroepitaxy. This work also demonstrates the need of a mesoscale tool for simulations of heteroepitaxy, as well as the unique advantage of the CAC method in simulation of the defect formation processes during heteroepitaxy.

Atomistic-informed phase field modeling of magnesium twin growth by disconnections

The nucleation and propagation of disconnections play an essential role during twin growth. Atomistic methods can reveal such small structural features on twin facets and model their motion, yet are limited by the simulation length and time scales. Alternatively, mesoscale modeling approaches (such as the phase field method) address these constraints of atomistic simulations and can maintain atomic-level accuracy when integrated with atomic-level information. In this work, a phase field model is used to simulate the disconnection-mediated twinning, informed by molecular dynamics (MD) simulations. This work considers the specific case of the growth of {101¯2} twin in magnesium. MD simulations are first conducted to obtain the orientation-dependent interface mobility and motion threshold, and to simulate twin embryo growth and collect facet velocities, which can be used for calibrating the continuum model. The phase field disconnections model, based on the principle of minimum dissipation potential, provides the theoretical framework. This model incorporates a nonconvex grain boundary energy, elasticity and shear coupling, and simulates disconnections as a natural emergence under the elastic driving force. The phase field model is further optimized by including the anisotropic interface mobility and motion threshold suggested by MD simulations. Results agree with MD simulations of twin embryo growth in the aspects of final twin thickness, twin shape, and twin size, as well as the kinetic behavior of twin boundaries and twin tips. The simulated twin microstructure is also consistent with experimental observations, demonstrating the fidelity of the model.

Elucidation of the Decomplexation and Reverse Migration Mechanism of Uranyl Ions from the Oil Phase to the Aqueous Phase Using Microsecond (0.2 μs) Molecular Dynamics Simulations.

Interphase ion transfer is ubiquitous in chemistry, physics, biology, and various engineering sciences. Ion transfer from the aqueous phase to the oil phase or vice versa is a complex chemical phenomenon, and its fundamental understanding is crucial for efficient and economical mass transfer. This ion transfer is much more complex for radionuclide metal ions. Therefore, an attempt has been made to elucidate the decomplexation and mechanism of reverse migration of uranyl ions from the loaded oil phase to the aqueous phase using large time-scale molecular dynamics simulations (microseconds) and density functional theory (DFT). The strength of the metal-ligand complex is the key factor for stripping among other mass transfer parameters. Stronger the metal-ligand complex, the lower the tendency for reverse migration into the aqueous phase, which was demonstrated by three different ligands for reverse migration using water as the stripping agent. The interaction energy of the metal-ligand complex has been calculated using DFT. The reverse migration is validated by the distance between the centers of mass. Higher interface thickness and lower interfacial tension belong to N,N,N',N'-tetra-octyldiglycolamide (TODGA), which are favorable for mass transfer across the liquid-liquid interface. The computed distribution coefficient (KD) is favorable for tributyl phosphate (TBP) and tri-iso-amyl phosphate (TiAP), whereas TODGA shows a higher KD, indicating a less favorable value for reverse migration. The distance between the uranyl ion and the TODGA molecule confirms that the entrapment of uranyl ions in the TODGA phase might be attributed to aggregation. Further, the aggregation tendency of TODGA molecules reduces the back extraction and the recovery of uranyl ions into the aqueous phase. The present MD results might be important for predicting an efficient back-extracting agent for the recovery of the metal ions from the oil phase.

Machine learning the electric field response of condensed phase systems using perturbed neural network potentials

The interaction of condensed phase systems with external electric fields is of major importance in a myriad of processes in nature and technology, ranging from the field-directed motion of cells (galvanotaxis), to geochemistry and the formation of ice phases on planets, to field-directed chemical catalysis and energy storage and conversion systems including supercapacitors, batteries and solar cells. Molecular simulation in the presence of electric fields would give important atomistic insight into these processes but applications of the most accurate methods such as ab-initio molecular dynamics (AIMD) are limited in scope by their computational expense. Here we introduce Perturbed Neural Network Potential Molecular Dynamics (PNNP MD) to push back the accessible time and length scales of such simulations. We demonstrate that important dielectric properties of liquid water including the field-induced relaxation dynamics, the dielectric constant and the field-dependent IR spectrum can be machine learned up to surprisingly high field strengths of about 0.2 V Å−1 without loss in accuracy when compared to ab-initio molecular dynamics. This is remarkable because, in contrast to most previous approaches, the two neural networks on which PNNP MD is based are exclusively trained on molecular configurations sampled from zero-field MD simulations, demonstrating that the networks not only interpolate but also reliably extrapolate the field response. PNNP MD is based on rigorous theory yet it is simple, general, modular, and systematically improvable allowing us to obtain atomistic insight into the interaction of a wide range of condensed phase systems with external electric fields.

Reactive Molecular Dynamics Simulations of Micrometeoroid Bombardment for Space Weathering of Asteroid (162173) Ryugu

Remote sensing observations by Hayabusa2 and laboratory measurements have revealed that the phyllosilicates on asteroid (162173) Ryugu are dehydrated/dehydroxylated due to space weathering. Reactive molecular dynamics simulations were performed to evaluate the magnitude of the dehydroxylation of Mg-rich serpentine by micrometeoroid impacts. When micrometeoroids were not coupled with interplanetary magnetic fields, serpentine could be dehydroxylated by micrometeoroids as small as 2 nm in size. In particular, ∼200 O–H bonds dissociated when the meteoroids were derived from cometary activity (the impact velocity was ∼20 km s−1). When nano-sized dust particles were accelerated to ∼300 km s−1 by the magnetic fields of solar wind plasma, the number of dissociated O–H bonds increased by 1 order of magnitude. Consequently, even 1 nm-sized dust particles can contribute to the space weathering of Ryugu. In all cases, Si–OH, H2O, and free OH were generated from the hydroxyls initially connected to Mg, which could partially offset dehydration. Despite the limitations of our computational resources, which restricted the simulation timescale to 1 ps, reactive molecular dynamic simulations demonstrated that micrometeoroid bombardment could influence the space weathering of asteroids.

Breaking the size limitation of nonadiabatic molecular dynamics in condensed matter systems with local descriptor machine learning

Nonadiabatic molecular dynamics (NA-MD) is a powerful tool to model far-from-equilibrium processes, such as photochemical reactions and charge transport. NA-MD application to condensed phase has drawn tremendous attention recently for development of next-generation energy and optoelectronic materials. Studies of condensed matter allow one to employ efficient computational tools, such as density functional theory (DFT) and classical path approximation (CPA). Still, system size and simulation timescale are strongly limited by costly ab initio calculations of electronic energies, forces, and NA couplings. We resolve the limitations by developing a fully machine learning (ML) approach in which all the above properties are obtained using neural networks based on local descriptors. The ML models correlate the target properties for NA-MD, implemented with DFT and CPA, directly to the system structure. Trained on small systems, the neural networks are applied to large systems and long timescales, extending NA-MD capabilities by orders of magnitude. We demonstrate the approach with dependence of charge trapping and recombination on defect concentration in MoS2. Defects provide the main mechanism of charge losses, resulting in performance degradation. Charge trapping slows with decreasing defect concentration; however, recombination exhibits complex dependence, conditional on whether it occurs between free or trapped charges, and relative concentrations of carriers and defects. Delocalized shallow traps can become localized with increasing temperature, changing trapping and recombination behavior. Completely based on ML, the approach bridges the gap between theoretical models and realistic experimental conditions and enables NA-MD on thousand-atom systems and many nanoseconds.

Unraveling the Roles of Ionic Size and Hydrogen Bonding in Electric Field-Driven Ion Emission from Hydroxylamine Nitrate-Based Ionic Liquids.

Ionic size and hydrogen bonding (HB) may play significant roles in controlling ion emission from HAN (hydroxylamine nitrate)-based ionic liquids (ILs) but have received little attention. In this paper, the ion emission behavior and mechanism in an external electric field are meticulously investigated using the molecular dynamics (MD) method and density functional theory. We find that the higher the proportion of ionic HAN in the blend of ILs, the longer the delay time of the ion start-up emission. In the positive mode, cations can evaporate directly from the surface of the studied ILs and manifest exclusively as the [EMIM]+ monomers within the simulation time scale, whereas in the negative mode, a variety of complicated anion clusters are emitted. As a result, the average charge-to-mass ratio of the positively charged species remarkably exceeds that of the negatively charged species. This large difference is attributed to the relatively larger size of the [EMIM]+ ion and the absence of substantial HB interactions between the [EMIM]+ ion and any other monomer, leading to diminished binding energies. Conversely, the strong HB interactions, primarily constituted by N-H--O and O-H--O hydrogen bonds, are clearly found in the [EtSO4]--based and HAN-based clusters. In addition, the [NO3]- and [EtSO4]- ions tend to combine with the small-sized [HA]+ ions to form large anion clusters rather than with the [EMIM]+ ions. The energy decomposition results further elucidate that the orbital interaction plays a pivotal role in the [NO3]- and [EtSO4]--based clusters. The findings clearly elucidate the experimental phenomena observed in previous studies and have implications for the formulation of multimode IL propellants.

Toward an ab Initio Description of Adsorbate Surface Dynamics.

The advent of machine learning potentials (MLPs) provides a unique opportunity to access simulation time scales and to directly compute physicochemical properties that are typically intractable using density functional theory (DFT). In this study, we use an active learning curriculum to train a generalizable MLP using the DeepMD-kit architecture. By using sufficiently long MLP-based molecular dynamics (MD) simulations, which provide DFT-level accuracy, we investigate the diffusion of key surface-bound adsorbates on a Ag(111) facet. Detailed analysis of the MLP/MD-calculated diffusivities sheds light on the potential shortcomings of using DFT-based nudged elastic band to estimate surface diffusion barriers. More generally, while this study is focused on a specific system, we anticipate that the underlying workflows and the resulting models can be extended to other adsorbates and other materials in the future.

A Euclidean transformer for fast and stable machine learned force fields

Recent years have seen vast progress in the development of machine learned force fields (MLFFs) based on ab-initio reference calculations. Despite achieving low test errors, the reliability of MLFFs in molecular dynamics (MD) simulations is facing growing scrutiny due to concerns about instability over extended simulation timescales. Our findings suggest a potential connection between robustness to cumulative inaccuracies and the use of equivariant representations in MLFFs, but the computational cost associated with these representations can limit this advantage in practice. To address this, we propose a transformer architecture called SO3krates that combines sparse equivariant representations (Euclidean variables) with a self-attention mechanism that separates invariant and equivariant information, eliminating the need for expensive tensor products. SO3krates achieves a unique combination of accuracy, stability, and speed that enables insightful analysis of quantum properties of matter on extended time and system size scales. To showcase this capability, we generate stable MD trajectories for flexible peptides and supra-molecular structures with hundreds of atoms. Furthermore, we investigate the PES topology for medium-sized chainlike molecules (e.g., small peptides) by exploring thousands of minima. Remarkably, SO3krates demonstrates the ability to strike a balance between the conflicting demands of stability and the emergence of new minimum-energy conformations beyond the training data, which is crucial for realistic exploration tasks in the field of biochemistry.

Determination of γ/γ' interface free energy for solid state precipitation in Ni-Al alloys from molecular dynamics simulation.

Interface free energy is a fundamental material parameter needed to predict the nucleation and growth of new phases. The high cost of experimentally determining this parameter makes it an ideal target for calculation through a physically informed simulation. Direct determination of interface free energy has many challenges, especially for solid-solid transformations. Indirect determination of the interface free energy from the nucleation data has been done in the case of solidification. However, a slow on molecular dynamics (MD) simulation time scale atomic diffusion makes this method not applicable to the case of nucleation from the solid phase when precipitate composition is different from that in matrix. To address this challenge, we outline the development of a new technique for determining the critical nucleus size from an MD simulation using a recently developed method to accelerate solid-state diffusion. The accuracy of our approach for the Ni-Al system for Ni3Al (γ') precipitates in a Ni-Al (γ) matrix is demonstrated well within experimental accuracy and greatly improves upon previous computational methods [Herrnring et al., Acta Mater. 215(8), 117053 (2021)].

Ligand Gaussian Accelerated Molecular Dynamics 3 (LiGaMD3): Improved Calculations of Binding Thermodynamics and Kinetics of Both Small Molecules and Flexible Peptides.

Binding thermodynamics and kinetics play critical roles in drug design. However, it has proven challenging to efficiently predict ligand binding thermodynamics and kinetics of small molecules and flexible peptides using conventional molecular dynamics (cMD), due to limited simulation time scales. Based on our previously developed ligand Gaussian accelerated molecular dynamics (LiGaMD) method, we present a new approach, termed "LiGaMD3″, in which we introduce triple boosts into three individual energy terms that play important roles in small-molecule/peptide dissociation, rebinding, and system conformational changes to improve the sampling efficiency of small-molecule/peptide interactions with target proteins. To validate the performance of LiGaMD3, MDM2 bound by a small molecule (Nutlin 3) and two highly flexible peptides (PMI and P53) were chosen as the model systems. LiGaMD3 could efficiently capture repetitive small-molecule/peptide dissociation and binding events within 2 μs simulations. The predicted binding kinetic constant rates and free energies from LiGaMD3 were in agreement with the available experimental values and previous simulation results. Therefore, LiGaMD3 provides a more general and efficient approach to capture dissociation and binding of both small-molecule ligands and flexible peptides, allowing for accurate prediction of their binding thermodynamics and kinetics.

GCMe: Efficient Implementation of the Gaussian Core Model with Smeared Electrostatic Interactions for Molecular Dynamics Simulations of Soft Matter Systems.

In recent years, molecular dynamics (MD) simulations have emerged as an essential tool for understanding the structure, dynamics, and phase behavior of charged soft matter systems. To explore phenomena across greater length and time scales in MD simulations, molecules are often coarse-grained for better computational performance. However, commonly used force fields represent particles as hard-core interaction centers with point charges, which often overemphasizes the packing effect and short-range electrostatics, especially in systems with bulky deformable organic molecules and systems with strong coarse-graining. This underscores the need for an efficient soft-core model to physically capture the effective interactions between coarse-grained particles. To this end, we implement a soft-core model uniting the Gaussian core model with smeared electrostatic interactions that is phenomenologically equivalent to recent theoretical models. We first parametrize it generically using water as the model solvent. Then, we benchmark its performance in the OpenMM toolkit for different boundary conditions to highlight a computational speedup of up to 34 × compared to commonly used force fields and existing implementations. Finally, we demonstrate its utility by investigating how boundary polarizability affects the adsorption behavior of a polyelectrolyte solution on perfectly conducting and nonmetal boundaries.

Molecular dynamics simulations of nucleosomes are coming of age

AbstractUnderstanding the function of eukaryotic genomes, including the human genome, is undoubtedly one of the major scientific challenges of the 21st century. The cornerstone of eukaryotic genome organization is nucleosomes—elementary building blocks of chromatin about 10 nm in size that wrap DNA around an octamer of histone proteins. Nucleosomes are integral players in all genomic processes, including transcription, DNA replication and repair. They mediate genome regulation at the epigenetic level, bridging the discrete nature of the genetic information encoded in DNA with the analog physical nature of the intermolecular interactions required to access that information. Due to their relatively large size and dynamic nature, nucleosomes are difficult objects for experimental characterization. Molecular dynamics (MD) simulations have emerged over the years as a useful tool to complement experimental studies. Particularly in recent years, advances in computing power, refinement of MD force fields and codes have opened up new frontiers in terms of simulation timescales and quality for nucleosomes and related systems. It has become possible to elucidate in atomistic detail their functional dynamics modes such as DNA unwrapping and sliding, to characterize the effects of epigenetic modifications, DNA and protein sequence variation on nucleosome structure and stability, to describe the mechanisms governing nucleosome interactions with chromatin‐associated proteins and the formation of supranucleosome structures. In this review, we systematically analyzed all‐atom MD simulation studies of nucleosomes and related structures published since 2018 and discussed their relevance in the context of older studies, experimental data, and related coarse‐grained and multiscale studies.This article is categorized under: Software > Molecular Modeling Molecular and Statistical Mechanics > Molecular Dynamics and Monte‐Carlo Methods Structure and Mechanism > Computational Biochemistry and Biophysics

Transformer enables ion transport behavior evolution and conductivity regulation for solid electrolyte

Ab initio molecular dynamics (AIMD) is an important technique for studying ion transport within solid electrolyte and interface effects between electrode and electrolyte, which is particularly critical for the rational design of new energy materials. However, AIMD is limited by the high-cost density functional theory (DFT) solution process and is unable to reach the time scale of the entire dynamic simulation, resulting in time-consuming AIMD calculations and a considerable scarcity of AIMD-based conductor data. Here, we propose a sequence relational large model based on transformer (T-AIMD) to infer ion diffusion from mean square displacement sequence data and hybrid multi-source material descriptor. T-AIMD successfully learns the whole long-range atomic diffusion to predict the ionic conductivity (σ) of any ion in any crystal structure to find fast-ion conductors, thus reducing the cost of AIMD simulation by a factor of 100. Using T-AIMD, we built the largest database of mixed ion conductors, and the σ of representative solid electrolytes has been successfully validated in previous battery experiments. Further, the manufactured solid-state battery with the predicted promising electrolyte exhibits almost no obvious capacity decay after 50 cycles with a high initial specific capacity of 1270 mAh g−1, which is promising to help devices work in extreme environments while guaranteeing battery life. By speeding up the prediction time of AIMD, the proposed T-AIMD opens the door for scientists to explore the atomic and molecular behaviors of other molecules/materials on long time scales, and will ultimately benefit the exploration of other key scientific questions in the energy field.

Structural pathways for ultrafast melting of optically excited thin polycrystalline Palladium films

Due to its extremely short timescale, the non-equilibrium melting of metals is exceptionally difficult to probe experimentally. The knowledge of melting mechanisms is thus based mainly on the results of theoretical predictions. This work reports on the investigation of ultrafast melting of thin polycrystalline Pd films studied by optical laser pump – X-ray free-electron laser probe experiments and molecular-dynamics simulations. By acquiring X-ray diffraction snapshots with sub-picosecond resolution, we capture the sample's atomic structure during its transition from the crystalline to the liquid state. Bridging the timescales of experiments and simulations allows us to formulate a realistic microscopic picture of the crystal-liquid transition. According to the experimental data, the melting process gradually accelerates with the increasing density of deposited energy. The molecular dynamics simulations reveal that the transition mechanism progressively varies from heterogeneous, initiated inside the material at structurally disordered grain boundaries, to homogenous, proceeding catastrophically in the crystal volume on a picosecond timescale comparable to that of electron-phonon coupling. We demonstrate that the existing models of strongly non-equilibrium melting, developed for systems with relatively weak electron-phonon coupling, remain valid even for ultrafast heating rates achieved in femtosecond laser-excited Pd. Furthermore, we highlight the role of pre-existing and transiently generated crystal defects in the transition to the liquid state.

Thermal degradation of greenhouse gas SF6 at realistic temperatures: Insights from atomic-scale CVHD simulations

Sulfur hexafluoride (SF6), recognized as a potent greenhouse gas with significant contributions to climate change, presents challenges in understanding its degradation processes. Molecular dynamics simulations are valuable tools for understanding modes of decomposition while the traditional approaches face limitations in time scale and require unrealistically high temperatures. The collective variable-driven hyperdynamics (CVHD) approach has been introduced to directly depict the pyrolysis process for SF6 gas at practical application temperatures, as low as 1600 K for the first time. Achieving an unprecedented acceleration factor of up to 107, the method extends the simulation time scale to milliseconds and beyond while maintaining consistency with experimental and theoretical models. The differences in the reaction process between simulations conducted at actual and elevated temperatures have been noted, providing insights into SF6 degradation pathways. The work provides a basis for the further studies on the thermal degradation of pollutants.

Integrating Explicit and Implicit Fullerene Models into UNRES Force Field for Protein Interaction Studies.

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.