Configurational Entropy in Ice Nanosystems: Tools for Structure Generation and Screening

2014 ◽  
Vol 10 (3) ◽  
pp. 1256-1264 ◽  
Author(s):  
P. Parkkinen ◽  
S. Riikonen ◽  
L. Halonen
Keyword(s):  
Configurational Entropy ◽  
Structure Generation

Combinatorial Design of Molecule using Activity-Linked Substructural Topological Information as Applied to Antitubercular Compounds

2018 ◽  
Vol 15 (1) ◽  
pp. 67-81 ◽  
Author(s):  
Chandan Raychaudhury ◽  
Md. Imbesat Hassan Rizvi ◽  
Debnath Pal
Keyword(s):  
Biological Activities ◽  
Combinatorial Design ◽  
Combinatorial Structure ◽  
Potential Drug ◽  
Structure Generation ◽  
Topological Information ◽  
Active Compounds ◽  
Antitubercular Drugs ◽  
Topological Distance ◽  
Drug Molecules

Background: Generating a large number of compounds using combinatorial methods increases the possibility of finding novel bioactive compounds. Although some combinatorial structure generation algorithms are available, any method for generating structures from activity-linked substructural topological information is not yet reported. Objective: To develop a method using graph-theoretical techniques for generating structures of antitubercular compounds combinatorially from activity-linked substructural topological information, predict activity and prioritize and screen potential drug candidates. </P><P> Methods: Activity related vertices are identified from datasets composed of both active and inactive or, differently active compounds and structures are generated combinatorially using the topological distance distribution associated with those vertices. Biological activities are predicted using topological distance based vertex indices and a rule based method. Generated structures are prioritized using a newly defined Molecular Priority Score (MPS). Results: Studies considering a series of Acid Alkyl Ester (AAE) compounds and three known antitubercular drugs show that active compounds can be generated from substructural information of other active compounds for all these classes of compounds. Activity predictions show high level of success rate and a number of highly active AAE compounds produced high MPS score indicating that MPS score may help prioritize and screen potential drug molecules. A possible relation of this work with scaffold hopping and inverse Quantitative Structure-Activity Relationship (iQSAR) problem has also been discussed. The proposed method seems to hold promise for discovering novel therapeutic candidates for combating Tuberculosis and may be useful for discovering novel drug molecules for the treatment of other diseases as well.

scholarly journals Random pore structure and REV scale flow analysis of engine particulate filter based on LBM

Open Physics ◽  
2020 ◽  
Vol 18 (1) ◽  
pp. 881-896
Author(s):  
Chunrui Wu ◽  
Tiechen Zhang ◽  
Jiale Fu ◽  
Xiaori Liu ◽  
Boxiong Shen
Keyword(s):  
Single Channel ◽  
Flow Analysis ◽  
Flow Characteristics ◽  
Axial Direction ◽  
Particulate Filter ◽  
Random Structure ◽  
Structure Generation ◽  
Inlet Flow ◽  
Random Structures ◽  
Boltzmann Method

Abstract In this article, lattice Boltzmann method (LBM) is used to simulate the multi-scale flow characteristics of the engine particulate filter at the pore scale and the representative elementary volume (REV) scale, respectively. Four kinds of random wall-pore structures are considered, which are circular random structure, square random structure, isotropic quartet structure generation set (QSGS), and anisotropic QSGS, with difference analysis done. In terms of the REV scale, the influence of different inlet flow velocities and wall permeabilities on the flow in single channel is analyzed. The result indicates that the internal seepage laws of random structures constructed in this article and single channel are in accordance with Darcy’s law. Circular random structure has better permeability than square random structure. Isotropic QSGS has better fluidity than anisotropic one. The flow in single channel is similar to Poiseuille flow. The flow lines in the channel are complicated and a large number of vortices appear at the ends of channel with high inlet flow rate. With the increase of inlet velocity, the static pressure in channel gradually increases along the axial direction as well as the seepage velocity. The temperature field in the channel becomes more uniform as the flow velocity increases, and the higher temperature distribution appears on the wall of the porous media.

Statistical Assessment of the Effects of Grain-Structure Representation and Micro-Properties on the Behavior of Bonded Block Models for Brittle Rock Damage Prediction

2021 ◽  
Vol 13 (14) ◽  
pp. 7889
Author(s):  
Carlos Efrain Contreras Inga ◽  
Gabriel Walton ◽  
Elizabeth Holley
Keyword(s):  
Grain Size ◽  
Grain Shape ◽  
Grain Structure ◽  
Minor Effect ◽  
Unique Contribution ◽  
Structure Generation ◽  
Structure Representation ◽  
The Real ◽  
Study Results ◽  
Block Models

The ability to predict the mechanical behavior of brittle rocks using bonded block models (BBM) depends on the accuracy of the geometrical representation of the grain-structure and the applied micro-properties. This paper evaluates the capabilities of BBMs for predictive purposes using an approach that employs published micro-properties in combination with a Voronoi BBM that properly approximates the real rock grain-structure. The Wausau granite, with Unconfined Compressive Strength (UCS) of 226 MPa and average grain diameter of 2 mm, is used to evaluate the effectiveness of the predictive approach. Four published sets of micro-properties calibrated for granites with similar mineralogy to the Wausau granite are used for the assessment. The effect of grain-structure representation in Voronoi BBMs is analyzed, considering grain shape, grain size and mineral arrangement. A unique contribution of this work is the explicit consideration of the effect of stochastic grain-structure generation on the obtained results. The study results show that the macro-properties of a rock can be closely replicated using the proposed approach. When using this approach, the micro-properties have a greater impact on the realism of the predictions than the specific grain-structure representation. The grain shape and grain size representations have a minor effect on the predictions for cases that do not deviate substantially from the real average grain geometry. However, the stochastic effect introduced by the use of randomly-generated Voronoi grain-structures can be significant, and this effect should be considered in future studies.

scholarly journals First-Principles Atomistic Thermodynamics and Configurational Entropy

2020 ◽  
Vol 8 ◽  
Author(s):  
Christopher Sutton ◽  
Sergey V. Levchenko
Keyword(s):  
First Principles ◽  
Configurational Entropy ◽  
Functional Materials ◽  
Expansion Method ◽  
Bulk Materials ◽  
Cluster Expansion Method ◽  
Particle Exchange ◽  
Properties Of Materials ◽  
Atomistic Thermodynamics ◽  
Statistical Effects

In most applications, functional materials operate at finite temperatures and are in contact with a reservoir of atoms or molecules (gas, liquid, or solid). In order to understand the properties of materials at realistic conditions, statistical effects associated with configurational sampling and particle exchange at finite temperatures must consequently be taken into account. In this contribution, we discuss the main concepts behind equilibrium statistical mechanics. We demonstrate how these concepts can be used to predict the behavior of materials at realistic temperatures and pressures within the framework of atomistic thermodynamics. We also introduce and discuss methods for calculating phase diagrams of bulk materials and surfaces as well as point defect concentrations. In particular, we describe approaches for calculating the configurational density of states, which requires the evaluation of the energies of a large number of configurations. The cluster expansion method is therefore also discussed as a numerically efficient approach for evaluating these energies.

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