On the Characterization of Inhomogeneity of the Density Distribution in Supercritical Fluids via Molecular Dynamics Simulation and Data Mining Analysis

Idriss, Hicham; Vyalov, I.; Georgi, N.; Киселев, М. Г.

doi:10.1021/jp404873a

Cited by 25 publications

(16 citation statements)

References 42 publications

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“…In this case, it is more effective to look at the local density rather than at the connectivity. The DBSCAN density‐based cluster algorithm is well‐suited for this task, and has been already used to study supercritical fluids and a two phase system of highly miscible fluids . The main idea of the algorithm is to include in a cluster all atoms with overlapping neighborhoods where the density is higher than a given threshold.…”

Section: Methodsmentioning

confidence: 99%

Pytim: A python package for the interfacial analysis of molecular simulations

et al. 2018

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Pytim is a versatile python framework for the analysis of interfacial properties in molecular simulations. The code implements several algorithms for the identification of instantaneous interfaces of arbitrary shape, and analysis tools written specifically for the study of interfacial properties, such as intrinsic profiles. The code is written in the python language, and makes use of the umpy and cipy packages to deliver high computational performances. Pytim relies on the MDAnalysis library to analyze the trajectory file formats of popular simulation packages such as gromacs, charmm, namd, lammps or Amber, and can be used to steer OpenMM simulations. Pytim can write information about surfaces and surface atomic layers to vtk, cube, and pdb files for easy visualization. The classes of Pytim can be easily customized and extended to include new interfacial algorithms or analysis tools. The code is available as open source and is free of charge. © 2018 The Authors. Journal of Computational Chemistry published by Wiley Periodicals, Inc

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Section: Methodsmentioning

confidence: 99%

Pytim: A python package for the interfacial analysis of molecular simulations

et al. 2018

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“…It should be noticed that in many previous works, it was pointed out that the high q 4 values are associated with the fact that the hydrogen bond network of water is not perfect but contains defects characterized geometrically by the presence of the fifth molecule in the first coordination shell. 59,[68][69][70][71] The extent of the variation of the high and low q 4 values is higher for water molecules near the hydrophobic group (Fig. 9).…”

Section: When a Linear Hydrogen Bond Occurs Between O W • • • X The mentioning

confidence: 94%

Local structure of dilute aqueous DMSO solutions, as seen from molecular dynamics simulations

Idrissi

Marekha

Barj

et al. 2017

The Journal of Chemical Physics

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The information about the structure of dimethyl sulfoxide (DMSO)-water mixtures at relatively low DMSO mole fractions is an important step in order to understand their cryoprotective properties as well as the solvation process of proteins and amino acids. Classical MD simulations, using the potential model combination that best reproduces the free energy of mixing of these compounds, are used to analyze the local structure of DMSO-water mixtures at DMSO mole fractions below 0.2. Significant changes in the local structure of DMSO are observed around the DMSO mole fraction of 0.1. The array of evidence, based on the cluster and the metric and topological parameters of the Voronoi polyhedra distributions, indicates that these changes are associated with the simultaneous increase of the number of DMSO-water and decrease of water-water hydrogen bonds with increasing DMSO concentration. The inversion between the dominance of these two types of H-bonds occurs around X = 0.1, above which the DMSO-DMSO interactions also start playing an important role. In other words, below the DMSO mole fraction of 0.1, DMSO molecules are mainly solvated by water molecules, while above it, their solvation shell consists of a mixture of water and DMSO. The trigonal, tetrahedral, and trigonal bipyramidal distributions of water shift to lower corresponding order parameter values indicating the loosening of these orientations. Adding DMSO does not affect the hydrogen bonding between a reference water molecule and its first neighbor hydrogen bonded water molecules, while it increases the bent hydrogen bond geometry involving the second ones. The close-packed local structure of the third, fourth, and fifth water neighbors also is reinforced. In accordance with previous theoretical and experimental data, the hydrogen bonding between water and the first, the second, and the third DMSO neighbors is stronger than that with its corresponding water neighbors. At a given DMSO mole fraction, the behavior of the intensity of the high orientational order parameter values indicates that water molecules are more ordered in the vicinity of the hydrophilic group while their structure is close-packed near the hydrophobic group of DMSO.

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“…DBSCAN had been employed to find representative structures from MD simulations, , as well as to find regions characterized by different molecular densities, , in this case using molecules as data points. HDBSCAN has recently gained attention also in the analysis of MD trajectories, − mostly due to its capability to reveal hierarchical structures.…”

Section: Clusteringmentioning

confidence: 99%

Unsupervised Learning Methods for Molecular Simulation Data

et al. 2021

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Unsupervised learning is becoming an essential tool to analyze the increasingly large amounts of data produced by atomistic and molecular simulations, in material science, solid state physics, biophysics, and biochemistry. In this Review, we provide a comprehensive overview of the methods of unsupervised learning that have been most commonly used to investigate simulation data and indicate likely directions for further developments in the field. In particular, we discuss feature representation of molecular systems and present state-of-the-art algorithms of dimensionality reduction , density estimation , and clustering , and kinetic models . We divide our discussion into self-contained sections, each discussing a specific method. In each section, we briefly touch upon the mathematical and algorithmic foundations of the method, highlight its strengths and limitations, and describe the specific ways in which it has been used-or can be used-to analyze molecular simulation data.

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On the Characterization of Inhomogeneity of the Density Distribution in Supercritical Fluids via Molecular Dynamics Simulation and Data Mining Analysis

Cited by 25 publications

References 42 publications

Pytim: A python package for the interfacial analysis of molecular simulations

Pytim: A python package for the interfacial analysis of molecular simulations

Local structure of dilute aqueous DMSO solutions, as seen from molecular dynamics simulations

Unsupervised Learning Methods for Molecular Simulation Data

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