Sonoluminescence is the phenomena of light emission from a collapsing gas bubble in a liquid. Theoretical explanations of this extreme energy focusing are controversial and difficult to validate experimentally. We propose to use molecular dynamics simulations of the collapsing gas bubble to clarify the energy focusing mechanism, and provide insight into the mechanism of light emission.
In this paper, we model the interior of a collapsing noble gas bubble as a hard sphere gas driven by a spherical piston boundary moving according to the Rayleigh-Plesset equation. We also include a simple treatment of ionization effects in the gas at high temperatures. By using fast, tree-based algorithms, we can exactly follow the dynamics of million particle systems during the collapse. Our results clearly show strong energy focusing within the bubble, including the formation of shocks, strong ionization, and temperatures in the range of 50,000---500,000 degrees Kelvin. Our calculations show that the gas-liquid boundary interaction has a strong effect on the internal gas dynamics. We also estimate the duration of the light pulse from our model, which predicts that it scales linearly with the ambient bubble radius.
As the number of particles in a physical sonoluminescing bubble is within the foreseeable capability of molecular dynamics simulations we also propose that fine scale sonoluminescence experiments can be viewed as excellent test problems for advancing the art of molecular dynamics.
Shenzhen Bay Laboratory（深圳湾实验室）；Freie Universität Berlin（柏林自由大学）；Peking University（北京大学）；Tangshan Normal University（唐山师范学院）
Deep learning is transforming many areas in science, and it has great potential in modeling molecular systems. However, unlike the mature deployment of deep learning in computer vision and natural language processing, its development in molecular modeling and simulations is still at an early stage, largely because the inductive biases of molecules are completely different from those of images or texts. Footed on these differences, we first reviewed the limitations of traditional deep learning models from the perspective of molecular physics, and wrapped up some relevant technical advancement a…
In this review, we highlight recent developments in the application of machine learning for molecular modeling and simulation. After giving a brief overview of the foundations, components, and workflow of a typical supervised learning approach for chemical problems, we showcase areas and state-of-the-art examples of their deployment. In this context, we discuss how machine learning relates to, supports, and augments more traditional physics-based approaches in computational research. We conclude by outlining challenges and future research directions that need to be addressed in order to make m…
Quaternions are an important tool to describe the orientation of a molecule. This paper considers the use of quaternions in matching two conformations of a molecule, in interpolating rotations, in performing statistics on orientational data, in the random sampling of rotations, and in establishing grids in orientation space. These examples show that many of the rotational problems that arise in molecular modeling may be handled simply and efficiently using quaternions.
At the Institute for Thermodynamics and Thermal Process Engineering (ITT) about 100 molecular models for pure substances have been developed so far. These models reproduce vapor pressure, saturated liquid density, and enthalpy of vaporization with a technical accuracy of less than 5%, less than 1%, and less than 5% deviation compared to experimental data, respectively. Simulation results are outlined for the pure substances ethylene oxide and ammonia, demonstrating the outstanding predictive power of the applied methods. Secondly, nucleation processes are discussed. Underlying effects on are s…
The complexity of molecular dynamics simulations necessitates dimension reduction and coarse-graining techniques to enable tractable computation. The generalized Langevin equation (GLE) describes coarse-grained dynamics in reduced dimensions. In spite of playing a crucial role in non-equilibrium dynamics, the memory kernel of the GLE is often ignored because it is difficult to characterize and expensive to solve. To address these issues, we construct a data-driven rational approximation to the GLE. Building upon previous work leveraging the GLE to simulate simple systems, we extend these resul…
A major challenge for molecular modeling consists in optimizing the unlike interaction potentials. In many cases, combination rules are generally suboptimal when accurate predictions of properties like the mixture vapor pressure are needed. However, the well known Lorentz-Berthelot rule performs quite well and can be used as a starting point. If more accurate results are required, it is advisable to adjust the dispersive interaction energy parameter. In the present study, mixture properties are explored for binary systems containing hydrogen bonding components. Furthermore, vapor-liquid interf…
University of Washington Bothell（华盛顿大学博塞尔分校）；University of Washington Seattle（华盛顿大学西雅图分校）；Saint Louis University（圣路易斯大学）；Pacific Lutheran University（太平洋路德大学）；University of Chicago（芝加哥大学）
Cryo-electron microscopy (cryo-EM) has become a major experimental technique to determine the structures of large protein complexes and molecular assemblies, as evidenced by the 2017 Nobel Prize. Although cryo-EM has been drastically improved to generate high-resolution three-dimensional (3D) maps that contain detailed structural information about macromolecules, the computational methods for using the data to automatically build structure models are lagging far behind. The traditional cryo-EM model building approach is template-based homology modeling. Manual de novo modeling is very time-con…
We discuss a novel use of the Geant4 simulation toolkit to model molecular transport in a vacuum environment, in the molecular flow regime. The Geant4 toolkit was originally developed by the high energy physics community to simulate the interactions of elementary particles within complex detector systems. Here its capabilities are utilized to model molecular vacuum transport in geometries where other techniques are impractical. The techniques are verified with an application representing a simple vacuum geometry that has been studied previously both analytically and by basic Monte Carlo simula…
A model for off-resonant microscopic cascading of (hyper)polarizabilities is developed using a self-consistent field approach to study mesoscopic systems of nonlinear polarizable atoms and molecules. We find enhancements in the higher-order susceptibilities resulting from geometrical and boundary orientation effects. We include an example of the dependence on excitation beam cross sectional structure and a simplified derivation of the microscopic cascading of the nonlinear optical response in guest-host systems.
Constructing atom-resolved states from low-resolution data is of practical importance in many areas of science and engineering. This problem is addressed in this paper in the context of multiscale factorization methods for molecular dynamics. These methods capture the crosstalk between atomic and coarse-grained scales arising in macromolecular systems. This crosstalk is accounted for by Trotter factorization, which is used to separate the all-atom from the coarse-grained phases of the computation. In this approach, short molecular dynamics runs are used to advance in time the coarse-grained va…
This paper presents an extended coarse-grained investigation of the elastic properties of polystyrene. In particular, we employ the well-known MARTINI force field and its modifications to perform extended molecular dynamics simulations at the $μ$s timescale, which take slow relaxation processes of polystyrene into account, such that the simulations permit analyzing the bulk modulus, the shear modulus, and the Poisson ratio. We show that through the iterative modification of MARTINI force field parameters it turns out to be possible to affect the shear modulus and the bulk modulus of the system…
Development of high strength carbon fibers (CFs) requires an understanding of the relationship between the processing conditions, microstructure and resulting properties. We developed a molecular model that combines kinetic Monte Carlo (KMC) and molecular dynamics (MD) techniques to predict the microstructure evolution during the carbonization process of carbon fiber manufacturing. The model accurately predicts the cross-sectional microstructure of carbon fibers, predicting features such as graphitic sheets and hairpin structures that have been observed experimentally. We predict the transvers…
We develop a mesoscopic lattice Boltzmann model for liquid-vapor phase transition by handling the microscopic molecular interaction. The short-range molecular interaction is incorporated by recovering an equation of state for dense gases, and the long-range molecular interaction is mimicked by introducing a pairwise interaction force. Double distribution functions are employed, with the density distribution function for the mass and momentum conservation laws and an innovative total kinetic energy distribution function for the energy conservation law. The recovered mesomacroscopic governing eq…
We apply the atom-atom potentials to molecular crystals of iron (II) complexes with bulky organic ligands. The crystals under study are formed by low-spin or high-spin molecules of Fe(phen)$_{2}$(NCS)$_{2}$ (phen = 1,10-phenanthroline), Fe(btz)$_{2}$(NCS)$_{2}$ (btz = 5,5$^{\prime }$,6,6$^{\prime}$-tetrahydro-4\textit{H},4$^{\prime}$\textit{H}-2,2$^{\prime }$-bi-1,3-thiazine), and Fe(bpz)$_{2}$(bipy) (bpz = dihydrobis(1-pyrazolil)borate, and bipy = 2,2$^{\prime}$-bipyridine). All molecular geometries are taken from the X-ray experimental data and assumed to be frozen. The unit cell dimensions …
A hallmark of meso-scale interfacial fluids is the multi-faceted, scale-dependent interfacial energy, which often manifests different characteristics across the molecular and continuum scale. The multi-scale nature imposes a challenge to construct reliable coarse-grained (CG) models, where the CG potential function needs to faithfully encode the many-body interactions arising from the unresolved atomistic interactions and account for the heterogeneous density distributions across the interface. We construct the CG models of both single- and two-component of polymeric fluid systems based on the…
Deep eutectic solvents (DESs) have gained attention in recent years as attractive alternatives to traditional solvents. There is a growing number of publications dealing with the thermodynamic modeling of DESs highlighting the importance of modeling the solutions' properties. In this review, we summarize the state-of-the-art in DES modeling as well as its current challenges. We also summarize the various modeling approaches to phase equilibria and properties of DESs with gE-models, EOS and molecular dynamics (MD) simulations. The current gE-model and EOS-based approaches handle DESs as pseudo-…
There is an increasing demand for computing the relevant structures, equilibria and long-timescale kinetics of biomolecular processes, such as protein-drug binding, from high-throughput molecular dynamics simulations. Current methods employ transformation of simulated coordinates into structural features, dimension reduction, clustering the dimension-reduced data, and estimation of a Markov state model or related model of the interconversion rates between molecular structures. This handcrafted approach demands a substantial amount of modeling expertise, as poor decisions at any step will lead …
Predicting molecular properties (e.g., atomization energy) is an essential issue in quantum chemistry, which could speed up much research progress, such as drug designing and substance discovery. Traditional studies based on density functional theory (DFT) in physics are proved to be time-consuming for predicting large number of molecules. Recently, the machine learning methods, which consider much rule-based information, have also shown potentials for this issue. However, the complex inherent quantum interactions of molecules are still largely underexplored by existing solutions. In this pape…
Spins in molecular magnets can experience both anisotropic exchange interactions and on-site magnetic anisotropy. In this paper we study the effect of exchange anisotropy on the molecular magnetic anisotropy both with and without on-site anisotropy. When both the anisotropies are small, we find that the axial anisotropy parameter $D_M$ in the effective spin Hamiltonian is the sum of the individual contributions due to exchange and on-site anisotropies. We find that even for axial anisotropy of about $15\%$, the low energy spectrum does not correspond to a single parent spin manifold but has in…
Kinetic modeling of molecular beam formation in a cryogenic buffer-gas cell
Vienna Center for Quantum Science and Technology, Atominstitut, TU Wien（维也纳量子科学与技术中心，原子研究所，维也纳工业大学）；LSS, Faculty of Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg（埃尔朗根-纽伦堡大学工程学院）；Institute of Space Systems, University of Stuttgart（斯图加特大学空间系统研究所）
