In the field of computational physics and material science, the efficient sampling of rare events... more In the field of computational physics and material science, the efficient sampling of rare events occurring at atomic scale is crucial. It aids in understanding mechanisms behind a wide range of important phenomena, including protein folding, conformal changes, chemical reactions and materials diffusion and deformation. Traditional simulation methods, such as Molecular Dynamics and Monte Carlo, often prove inefficient in capturing the timescale of these rare events by brute force. In this paper, we introduce a practical approach by combining the idea of importance sampling with deep neural networks (DNNs) that enhance the sampling of these rare events. In particular, we approximate the variance-free bias potential function with DNNs which is trained to maximize the probability of rare event transition under the importance potential function. This method is easily scalable to high-dimensional problems and provides robust statistical guarantees on the accuracy of the estimated probability of rare event transition. Furthermore, our algorithm can actively generate and learn from any successful samples, which is a novel improvement over existing methods. Using a 2D system as a test bed, we provide comparisons between results obtained from different training strategies, traditional Monte Carlo sampling and numerically solved optimal bias potential function under different temperatures. Our numerical results demonstrate the efficacy of the DNN-based importance sampling of rare events. 1 Introduction Contemporary machine learning models suffer a substantial degradation in performance when confronted with long-tail events that are not represented well in collected data [1-3]. Several approaches, including the detection of out-of-distribution samples [4] and data resampling techniques [5, 6], can address this issue. In this paper, we propose a method that is specifically designed to efficiently sample these long-tail events, also referred to as rare events. Our method aims to efficiently collect rare events in simulation and increase their representation in datasets. Specifically, in the domains of materials science and biochemistry , there is a pressing need to sample rare events that are associated with specific physical phenomena and estimate their probabilities. This is critical in advancing our understanding of various materials properties ranging from mechanical composites [7-9] to transport proteins [10], which are essential to aerospace and pharmaceutical 37th Conference on Neural Information Processing Systems (NeurIPS 2023).
Discrete dislocation dynamics (DDD) simulations offer valuable insights into the plastic deformat... more Discrete dislocation dynamics (DDD) simulations offer valuable insights into the plastic deformation and workhardening behavior of metals by explicitly modeling the evolution of dislocation lines under stress. However, the computational cost associated with calculating forces due to the long-range elastic interactions between dislocation segment pairs is one of the main causes that limit the achievable strain levels in DDD simulations. These elastic interaction forces can be obtained either from the integral of the stress field due to one segment over the other segment, or from the derivatives of the elastic interaction energy. In both cases, the results involve a double-integral over the two interacting segments. Currently, existing DDD simulations employ the stress-based approach with both integrals evaluated either from analytical expressions or from numerical quadrature. In this study, we systematically analyze the accuracy and computational cost of the stress-based and energy-based approaches with different ways of evaluating the integrals. We find that the stress-based approach is more efficient than the energy-based approach. Furthermore, the stress-based approach becomes most cost-effective when one integral is evaluated from analytic expression and the other integral from numerical quadrature. For well-separated segment pairs whose center distances are more than three times their lengths, this one-analytic-integral and one-numerical-integral approach is more than three times faster than the fully analytic approach, while the relative error in the forces is less than 10 −3. Because the vast majority of segment pairs in a typical simulation cell are well-separated, we expect the hybrid analytic/numerical approach to significantly boost the numerical efficiency of DDD simulations of work hardening.
for providing unparalleled support and unmatched memories to be treasured for the whole life. I a... more for providing unparalleled support and unmatched memories to be treasured for the whole life. I also want to thank Sultan, Noor, Amwar, Mubashshar, Ghazi, Usloob, Imran, Shahanwaz, and many other friends I was fortunate enough to have at different stages of my life. I also want to express my respect for my two childhood teachers, Mohammad Yunus and Om Prakash Sharma, and my M.Tech. advisor Prof. Sumit Basu who played significant roles in motivating me to pursue higher educations. I want to express my unutterable respect to my parents, Naseema Khatoon and Mohammad Athar, and my brothers, Abrar, Irshad, Zeeshan, and Enayat, my sister-in-law Asma-ul-Husna for all their countless sacrifices, continuous encouragement, and unconditional support. I want to thank my niece Zehra for bringing me moments of joy and happiness through her ineffable innocence. This thesis owes its materialization to and imprinted by the persons mentioned above, and several others whom I apologize for forgetting to mention.
for providing unparalleled support and unmatched memories to be treasured for the whole life. I a... more for providing unparalleled support and unmatched memories to be treasured for the whole life. I also want to thank Sultan, Noor, Amwar, Mubashshar, Ghazi, Usloob, Imran, Shahanwaz, and many other friends I was fortunate enough to have at different stages of my life. I also want to express my respect for my two childhood teachers, Mohammad Yunus and Om Prakash Sharma, and my M.Tech. advisor Prof. Sumit Basu who played significant roles in motivating me to pursue higher educations. I want to express my unutterable respect to my parents, Naseema Khatoon and Mohammad Athar, and my brothers, Abrar, Irshad, Zeeshan, and Enayat, my sister-in-law Asma-ul-Husna for all their countless sacrifices, continuous encouragement, and unconditional support. I want to thank my niece Zehra for bringing me moments of joy and happiness through her ineffable innocence. This thesis owes its materialization to and imprinted by the persons mentioned above, and several others whom I apologize for forgetting to mention.
International Journal of Pure & Applied Bioscience, 2015
The experiment was conducted to know the efficacy of different bio control agents at different co... more The experiment was conducted to know the efficacy of different bio control agents at different concentrations against growth of Alternaria alternata, causing leaf blight of Adhatoda zeylanica Medic. Different bio control agents in different solvents were tested in vitro, among all leaves extracts Polyalthiya longifolia was found to be more effective and inhibited cent per cent fungal growth i.e. 100% at 3.0 % concentration in alcoholic form followed by acetone leaves extract at 4.5 % concentration. However, least mycelial growth inhibition was recorded in aqueous leaves extract concentration. Key words: Adhatoda zeylanica , Alternaria alternate, Biocontrol, Polyalthiya longifolia.
This research is an attempt to revelate the rule of language in developing and sustenate nation's... more This research is an attempt to revelate the rule of language in developing and sustenate nation's culture, especially the nations who have a national ancient culture like Kurd. The above little considers the Kurdish Language for sustenate an unstate Kurdish nation in order to remains as a national different custom among the neighborhood nations, and these will be ensured in the all scopes with the using of mother tongue. The right of using mother tongue was proved directly or indirectly in all religions, world agreements, human rights and democratic state constitutions. In other word there is no justification to prohibit the right of using mother tongue in the different scopes of society. Language is a basic factor for remaining and the perishing the nations, it is also a main dimension of constructing the ken of the nation. Looking at the history will show us that there were many languages disappeared due to political reasons or neglecting. Kurd as an ancient nation has its own language which consists of some different dialects, but the dominant tried to perish it, by some means or other a kind of language assimilation was tried, but the reaction was there to defend and protect the language. Now Kurdish language is spoken only in a part of Kurdistan "South Kurdistan", beside Arabic Language Kurdish was also confessed in Iraqi constitution. This research which is fallen in with apply linguistics, prescriptive explanations will be performed. The research consists of an introduction and two chapters. n the first chapter language and Kurdish language, the connection between language and nations and the characteristics of a national language will be talked about. The second chapter is dedicated to talk about mother language and using mother language as a sustained right, and proving this cosmical in the international agreements and democratic state constitutions. Meanwhile exposition of the importance of using Kurdish national language in all stages especially in the education in both governmental and nongovernmental sectors. At the end the most important gained results and the list of bibliography will be showed.
Abstract Solute accelerated cross-slip of pyramidal 〈 c + a 〉 screw dislocations has recently bee... more Abstract Solute accelerated cross-slip of pyramidal 〈 c + a 〉 screw dislocations has recently been recognized as a crucial mechanism in enhancing the ductility of solid-solution Mg alloys. In pure Mg, cross-slip is ineffective owing to the energy difference between the high energy pyramidal I and low energy pyramidal II 〈 c + a 〉 screw dislocations. A small addition of solutes, especially rare earth (RE) elements, can reduce this energy difference and accelerate cross-slip, thus enabling enhanced ductility. With increasing solute concentrations, the pyramidal I dislocation can become energetically favorable, which switches the primary 〈 c + a 〉 slip plane and alters the cross-slip process. Here, the transition path and energetics for double cross-slip of pyramidal I 〈 c + a 〉 dislocations are analysed in the regime where the pyramidal I dislocation is energetically more favorable than the pyramidal II. This is achieved using nudged elastic band simulations on a proxy MEAM potential for Mg designed to favor the pyramidal I over pyramidal II. The minimum energy transition path for pyramidal I double cross-slip is found to initiate with cross-slip onto a pyramidal II plane followed by cross-slip onto a pyramidal I plane parallel to the original pyramidal I plane. A previous mechanistic model for ductility is then extended to higher solute concentrations where pyramidal I is favorable. The model predicts an upper limit of solute concentrations beyond which ductility again becomes poor in Mg alloys. The model predictions are consistent with limited experiments of Mg-RE alloys at high concentrations and motivate further experimental studies in the high concentration regime.
The thermally activated pyramidal-to-basal (PB) transition of c + a dislocations, transforming gl... more The thermally activated pyramidal-to-basal (PB) transition of c + a dislocations, transforming glissile pyramidal dissociated core structures into sessile basal dissociated ones, lies at the origin of low ductility in pure magnesium (Mg). Solute-accelerated cross-slip and double cross-slip of pyramidal c + a dislocations have recently been proposed as a mechanism that can circumvent the deleterious effects of the PB transition by enabling rapid dislocation multiplication and isolating PB-transformed sessile segments. Here, the theory for solute-accelerated cross-slip is revisited with an explicit atomistic derivation, is extended to include multiple very dilute solute concentrations, and various aspects of the theory are demonstrated computationally. DFT inputs to the theory for a wide range of new alloying elements are presented. The theory is validated by comparing predicted ductility to literature experiments for a range of alloys. The theory is then applied to predict composition ranges for ductility in rare-earth free ternary and quaternary dilute alloys. The wide range of new alloys predicted to be ductile can serve as a guide to experimental development of new ductile Mg alloys.
Modelling and Simulation in Materials Science and Engineering, Jul 20, 2018
An interatomic potential for the Mg–Y binary system is developed within the framework of the seco... more An interatomic potential for the Mg–Y binary system is developed within the framework of the second-nearest-neighbor modified embedded-atom method (MEAM) based on a very good MEAM potential for pure Mg. The Mg–Y potential is fitted to a range of key physical properties, either experimental or computed by first-principles methods, including the Y interaction energy with basal and pyramidal stacking faults, and properties of the B2 Mg–Y intermetallic phase. Reasonable agreement is obtained—much better than existing potentials in the literature—but differences remain for subtle but important aspects of Y solutes in Mg. The predictions of the potential for Y misfit volume in Mg, Y solute interactions with the pyramidal II (c + a) edge dislocation and {1012} tension-twin boundary are then compared against recent density functional theory results, and reasonable accuracy is obtained. In light of the spectrum of results presented here, the applicability and limitations of this Mg–Y MEAM potential for investigating various plasticity phenomena in Mg–Y solid solution alloys are carefully discussed.
The Mg–Y binary system's potential has been developed within the framework of the second-near... more The Mg–Y binary system's potential has been developed within the framework of the second-nearest-neighbor modified embedded-atom method (MEAM) based on a potential for pure Mg. The potential fitting is done on a range of physical properties, either experimental or computed by first-principles methods. These include the Y interaction energy with basal and pyramidal stacking faults and properties of the B2 Mg–Y intermetallic phase. Using this model, one can make predictions generally in reasonable agreement with experiments and or DFT, but differences remain for subtle but important aspects of Y solutes in Mg.
Abstract Solute accelerated cross-slip of pyramidal 〈 c + a 〉 screw dislocations has recently bee... more Abstract Solute accelerated cross-slip of pyramidal 〈 c + a 〉 screw dislocations has recently been recognized as a crucial mechanism in enhancing the ductility of solid-solution Mg alloys. In pure Mg, cross-slip is ineffective owing to the energy difference between the high energy pyramidal I and low energy pyramidal II 〈 c + a 〉 screw dislocations. A small addition of solutes, especially rare earth (RE) elements, can reduce this energy difference and accelerate cross-slip, thus enabling enhanced ductility. With increasing solute concentrations, the pyramidal I dislocation can become energetically favorable, which switches the primary 〈 c + a 〉 slip plane and alters the cross-slip process. Here, the transition path and energetics for double cross-slip of pyramidal I 〈 c + a 〉 dislocations are analysed in the regime where the pyramidal I dislocation is energetically more favorable than the pyramidal II. This is achieved using nudged elastic band simulations on a proxy MEAM potential for Mg designed to favor the pyramidal I over pyramidal II. The minimum energy transition path for pyramidal I double cross-slip is found to initiate with cross-slip onto a pyramidal II plane followed by cross-slip onto a pyramidal I plane parallel to the original pyramidal I plane. A previous mechanistic model for ductility is then extended to higher solute concentrations where pyramidal I is favorable. The model predicts an upper limit of solute concentrations beyond which ductility again becomes poor in Mg alloys. The model predictions are consistent with limited experiments of Mg-RE alloys at high concentrations and motivate further experimental studies in the high concentration regime.
The thermally activated pyramidal-to-basal (PB) transition of c + a dislocations, transforming gl... more The thermally activated pyramidal-to-basal (PB) transition of c + a dislocations, transforming glissile pyramidal dissociated core structures into sessile basal dissociated ones, lies at the origin of low ductility in pure magnesium (Mg). Solute-accelerated cross-slip and double cross-slip of pyramidal c + a dislocations have recently been proposed as a mechanism that can circumvent the deleterious effects of the PB transition by enabling rapid dislocation multiplication and isolating PB-transformed sessile segments. Here, the theory for solute-accelerated cross-slip is revisited with an explicit atomistic derivation, is extended to include multiple very dilute solute concentrations, and various aspects of the theory are demonstrated computationally. DFT inputs to the theory for a wide range of new alloying elements are presented. The theory is validated by comparing predicted ductility to literature experiments for a range of alloys. The theory is then applied to predict composition ranges for ductility in rare-earth free ternary and quaternary dilute alloys. The wide range of new alloys predicted to be ductile can serve as a guide to experimental development of new ductile Mg alloys.
International Journal of Multicultural and Multireligious Understanding, 2020
This study aimed to examine the effect of strategic planning that is represented by three dimensi... more This study aimed to examine the effect of strategic planning that is represented by three dimensions (top management support, technology and strategic goals) on entrepreneurship strategy requirements that is represented by three dimensions (creative capabilities, risk taking,and entrepreneurial culture) related to the perspectives of employees at private hospitals in Iraqi Kurdistan Region Erbil city. A survey questionnaire has been used to collect data, and the questionnaires distributed randomly to (150) health staff comprising of a number of private hospitals, (146) of staff were able to fill and return the questionnaires however (142) of the questionnaires was suitable for the purpose of statically analyzing. The questionnaire encompassed two sections with 30 closed-ended questions. Data collected analyzed quantitatively by using SPSS program version 20. The results of study concluded that there is a moderate and positive correlation as well as a significant impact of strategic ...
Effective permeability is a key physical property of porous media that defines its ability to tra... more Effective permeability is a key physical property of porous media that defines its ability to transport fluid. Digital rock physics combines modern tomographic imaging techniques with advanced numerical simulations to estimate effective rock properties. Digital rock physics is used to complement or replace expensive and time-consuming or impractical laboratory measurements. However, with increase in sample size to capture multimodal and multiscale microstructures, conventional approaches based on direct numerical simulation (DNS) are becoming very computationally intensive or even infeasible. To address this computational challenge, we propose a hierarchical homogenization method (HHM) with a data-driven surrogate model based on 3-D convolutional neural network (CNN) and transfer learning to estimate effective permeability of digital rocks with large sample sizes up to billions of voxels. This workflow (HHM-CNN) divides the large digital rock into small sub-volumes and predicts the sub-volume permeabilities through a CNN surrogate model of Stokes flow at the pore scale. The effective permeability of the full digital rock is then predicted by solving the Darcy equations efficiently on the upscaled model in which the permeability of each cell is assigned by the surrogate model. The proposed method is verified on micro-CT data of both sandstones and carbonates as well as the reconstructed high-resolution digital rock obtained by multiscale data fusion. The computed permeabilities of our proposed hierarchical approach are consistent with the results of the DNS on the full digital rock. Compared with conventional DNS algorithms, the proposed hierarchical approach can largely reduce the computational time and memory demand.
Modelling and Simulation in Materials Science and Engineering, Jul 22, 2022
Taking advantage of the advances in generative deep learning, particularly normalizing flow, a fr... more Taking advantage of the advances in generative deep learning, particularly normalizing flow, a framework, called Boltzmann Generator, has recently been proposed for the purpose of generating equilibrium atomic configurations from the canonical ensemble and determining the associated free energy. In this work, we revisit Boltzmann Generator to motivate the construction of the loss function from the statistical mechanical point of view, and to cast the training of the neural networks in purely unsupervised manner that requires no samples of the atomic configurations from the equilibrium ensemble. We further show that the normalizing flow framework furnishes a reference thermodynamic system, very close to the real thermodynamic system under consideration, that is suitable for the well-established free energy perturbation methods to determine accurate free energy of solids. We then apply the normalizing flow to two problems: temperature-dependent Gibbs free energy of perfect crystal and formation free energy of monovacancy defect in a model system of diamond cubic Si. The results obtained from the normalizing flow are shown to be in good agreement with that obtained from independent well-established free energy methods.
Taking advantage of the advances in generative deep learning, particularly normalizing flow, a fr... more Taking advantage of the advances in generative deep learning, particularly normalizing flow, a framework, called Boltzmann Generator, has recently been proposed for the purpose of generating equilibrium atomic configurations from the canonical ensemble and determining the associated free energy. In this work, we revisit Boltzmann Generator to motivate the construction of the loss function from the statistical mechanical point of view, and to cast the training of the neural networks in purely unsupervised manner that requires no samples of the atomic configurations from the equilibrium ensemble. We further show that the normalizing flow framework furnishes a reference thermodynamic system, very close to the real thermodynamic system under consideration, that is suitable for the well-established free energy perturbation methods to determine accurate free energy of solids. We then apply the normalizing flow to two problems: temperature-dependent Gibbs free energy of perfect crystal and formation free energy of monovacancy defect in a model system of diamond cubic Si. The results obtained from the normalizing flow are shown to be in good agreement with that obtained from independent well-established free energy methods.
Determining effective elastic properties of rocks from their pore-scale digital images is a key g... more Determining effective elastic properties of rocks from their pore-scale digital images is a key goal of digital rock physics (DRP). Direct numerical simulation (DNS) of elastic behavior, however, incurs high computational cost; and surrogate machine learning (ML) model, particularly convolutional neural network (CNN), show promises to accelerate homogenization process. 3D CNN models, however, are unable to handle large images due to memory issues. To address this challenge, we propose a novel method that combines 3D CNN with hierarchical homogenization method (HHM). The surrogate 3D CNN model homogenizes only small subimages, and a DNS is used to homogenize the intermediate image obtained by assembling small subimages. The 3D CNN model is designed to output the homogenized elastic constants within the Hashin-Shtrikman (HS) bounds of the input images. The 3D CNN model is first trained on data comprising equal proportions of five sandstone (quartz mineralogy) images, and, subsequently, fine-tuned for specific rocks using transfer learning. The proposed method is applied to homogenize the rock images of size 300×300×300 and 600×600×600 voxels, and the predicted homogenized elastic moduli are shown to agree with that obtained from the brute-force DNS. The transferability of the trained 3D CNN model (using transfer learning) is further demonstrated by predicting the homogenized elastic moduli of a limestone rock with calcite mineralogy. The surrogate 3D CNN model in combination with the HHM is thus shown to be a promising tool for the homogenization of large 3D digital rock images and other random media.
This work focuses on computing the homogenized elastic properties of rocks from 3D micro-computed... more This work focuses on computing the homogenized elastic properties of rocks from 3D micro-computed-tomography (micro-CT) scanned images. The accurate computation of homogenized properties of rocks, archetypal random media, requires both resolution of intricate underlying microstructure and large field of view, resulting in huge micro-CT images. Homogenization entails solving the local elasticity problem computationally which can be prohibitively expensive for a huge image. To mitigate this problem, we use a renormalization method inspired scheme, the hierarchical homogenization method, where a large image is partitioned into smaller subimages. The individual subimages are separately homogenized using periodic boundary conditions, and then assembled into a much smaller intermediate image. The intermediate image is again homogenized, subject to the periodic boundary condition, to find the final homogenized elastic constant of the original image. An FFT-based elasticity solver is used to solve the associated periodic elasticity problem. The error in the homogenized elastic constant is empirically shown to follow a power law scaling with exponent −1 with respect to the subimage size across all five microstructures of rocks. We further show that the inclusion of surrounding materials during the homogenization of the small subimages reduces error in the final homogenized elastic moduli while still respecting the power law with the exponent of −1. This power law scaling is then exploited to determine a better approximation of the large heterogeneous microstructures based on Richardson extrapolation.
In the field of computational physics and material science, the efficient sampling of rare events... more In the field of computational physics and material science, the efficient sampling of rare events occurring at atomic scale is crucial. It aids in understanding mechanisms behind a wide range of important phenomena, including protein folding, conformal changes, chemical reactions and materials diffusion and deformation. Traditional simulation methods, such as Molecular Dynamics and Monte Carlo, often prove inefficient in capturing the timescale of these rare events by brute force. In this paper, we introduce a practical approach by combining the idea of importance sampling with deep neural networks (DNNs) that enhance the sampling of these rare events. In particular, we approximate the variance-free bias potential function with DNNs which is trained to maximize the probability of rare event transition under the importance potential function. This method is easily scalable to high-dimensional problems and provides robust statistical guarantees on the accuracy of the estimated probability of rare event transition. Furthermore, our algorithm can actively generate and learn from any successful samples, which is a novel improvement over existing methods. Using a 2D system as a test bed, we provide comparisons between results obtained from different training strategies, traditional Monte Carlo sampling and numerically solved optimal bias potential function under different temperatures. Our numerical results demonstrate the efficacy of the DNN-based importance sampling of rare events. 1 Introduction Contemporary machine learning models suffer a substantial degradation in performance when confronted with long-tail events that are not represented well in collected data [1-3]. Several approaches, including the detection of out-of-distribution samples [4] and data resampling techniques [5, 6], can address this issue. In this paper, we propose a method that is specifically designed to efficiently sample these long-tail events, also referred to as rare events. Our method aims to efficiently collect rare events in simulation and increase their representation in datasets. Specifically, in the domains of materials science and biochemistry , there is a pressing need to sample rare events that are associated with specific physical phenomena and estimate their probabilities. This is critical in advancing our understanding of various materials properties ranging from mechanical composites [7-9] to transport proteins [10], which are essential to aerospace and pharmaceutical 37th Conference on Neural Information Processing Systems (NeurIPS 2023).
Discrete dislocation dynamics (DDD) simulations offer valuable insights into the plastic deformat... more Discrete dislocation dynamics (DDD) simulations offer valuable insights into the plastic deformation and workhardening behavior of metals by explicitly modeling the evolution of dislocation lines under stress. However, the computational cost associated with calculating forces due to the long-range elastic interactions between dislocation segment pairs is one of the main causes that limit the achievable strain levels in DDD simulations. These elastic interaction forces can be obtained either from the integral of the stress field due to one segment over the other segment, or from the derivatives of the elastic interaction energy. In both cases, the results involve a double-integral over the two interacting segments. Currently, existing DDD simulations employ the stress-based approach with both integrals evaluated either from analytical expressions or from numerical quadrature. In this study, we systematically analyze the accuracy and computational cost of the stress-based and energy-based approaches with different ways of evaluating the integrals. We find that the stress-based approach is more efficient than the energy-based approach. Furthermore, the stress-based approach becomes most cost-effective when one integral is evaluated from analytic expression and the other integral from numerical quadrature. For well-separated segment pairs whose center distances are more than three times their lengths, this one-analytic-integral and one-numerical-integral approach is more than three times faster than the fully analytic approach, while the relative error in the forces is less than 10 −3. Because the vast majority of segment pairs in a typical simulation cell are well-separated, we expect the hybrid analytic/numerical approach to significantly boost the numerical efficiency of DDD simulations of work hardening.
for providing unparalleled support and unmatched memories to be treasured for the whole life. I a... more for providing unparalleled support and unmatched memories to be treasured for the whole life. I also want to thank Sultan, Noor, Amwar, Mubashshar, Ghazi, Usloob, Imran, Shahanwaz, and many other friends I was fortunate enough to have at different stages of my life. I also want to express my respect for my two childhood teachers, Mohammad Yunus and Om Prakash Sharma, and my M.Tech. advisor Prof. Sumit Basu who played significant roles in motivating me to pursue higher educations. I want to express my unutterable respect to my parents, Naseema Khatoon and Mohammad Athar, and my brothers, Abrar, Irshad, Zeeshan, and Enayat, my sister-in-law Asma-ul-Husna for all their countless sacrifices, continuous encouragement, and unconditional support. I want to thank my niece Zehra for bringing me moments of joy and happiness through her ineffable innocence. This thesis owes its materialization to and imprinted by the persons mentioned above, and several others whom I apologize for forgetting to mention.
for providing unparalleled support and unmatched memories to be treasured for the whole life. I a... more for providing unparalleled support and unmatched memories to be treasured for the whole life. I also want to thank Sultan, Noor, Amwar, Mubashshar, Ghazi, Usloob, Imran, Shahanwaz, and many other friends I was fortunate enough to have at different stages of my life. I also want to express my respect for my two childhood teachers, Mohammad Yunus and Om Prakash Sharma, and my M.Tech. advisor Prof. Sumit Basu who played significant roles in motivating me to pursue higher educations. I want to express my unutterable respect to my parents, Naseema Khatoon and Mohammad Athar, and my brothers, Abrar, Irshad, Zeeshan, and Enayat, my sister-in-law Asma-ul-Husna for all their countless sacrifices, continuous encouragement, and unconditional support. I want to thank my niece Zehra for bringing me moments of joy and happiness through her ineffable innocence. This thesis owes its materialization to and imprinted by the persons mentioned above, and several others whom I apologize for forgetting to mention.
International Journal of Pure & Applied Bioscience, 2015
The experiment was conducted to know the efficacy of different bio control agents at different co... more The experiment was conducted to know the efficacy of different bio control agents at different concentrations against growth of Alternaria alternata, causing leaf blight of Adhatoda zeylanica Medic. Different bio control agents in different solvents were tested in vitro, among all leaves extracts Polyalthiya longifolia was found to be more effective and inhibited cent per cent fungal growth i.e. 100% at 3.0 % concentration in alcoholic form followed by acetone leaves extract at 4.5 % concentration. However, least mycelial growth inhibition was recorded in aqueous leaves extract concentration. Key words: Adhatoda zeylanica , Alternaria alternate, Biocontrol, Polyalthiya longifolia.
This research is an attempt to revelate the rule of language in developing and sustenate nation's... more This research is an attempt to revelate the rule of language in developing and sustenate nation's culture, especially the nations who have a national ancient culture like Kurd. The above little considers the Kurdish Language for sustenate an unstate Kurdish nation in order to remains as a national different custom among the neighborhood nations, and these will be ensured in the all scopes with the using of mother tongue. The right of using mother tongue was proved directly or indirectly in all religions, world agreements, human rights and democratic state constitutions. In other word there is no justification to prohibit the right of using mother tongue in the different scopes of society. Language is a basic factor for remaining and the perishing the nations, it is also a main dimension of constructing the ken of the nation. Looking at the history will show us that there were many languages disappeared due to political reasons or neglecting. Kurd as an ancient nation has its own language which consists of some different dialects, but the dominant tried to perish it, by some means or other a kind of language assimilation was tried, but the reaction was there to defend and protect the language. Now Kurdish language is spoken only in a part of Kurdistan "South Kurdistan", beside Arabic Language Kurdish was also confessed in Iraqi constitution. This research which is fallen in with apply linguistics, prescriptive explanations will be performed. The research consists of an introduction and two chapters. n the first chapter language and Kurdish language, the connection between language and nations and the characteristics of a national language will be talked about. The second chapter is dedicated to talk about mother language and using mother language as a sustained right, and proving this cosmical in the international agreements and democratic state constitutions. Meanwhile exposition of the importance of using Kurdish national language in all stages especially in the education in both governmental and nongovernmental sectors. At the end the most important gained results and the list of bibliography will be showed.
Abstract Solute accelerated cross-slip of pyramidal 〈 c + a 〉 screw dislocations has recently bee... more Abstract Solute accelerated cross-slip of pyramidal 〈 c + a 〉 screw dislocations has recently been recognized as a crucial mechanism in enhancing the ductility of solid-solution Mg alloys. In pure Mg, cross-slip is ineffective owing to the energy difference between the high energy pyramidal I and low energy pyramidal II 〈 c + a 〉 screw dislocations. A small addition of solutes, especially rare earth (RE) elements, can reduce this energy difference and accelerate cross-slip, thus enabling enhanced ductility. With increasing solute concentrations, the pyramidal I dislocation can become energetically favorable, which switches the primary 〈 c + a 〉 slip plane and alters the cross-slip process. Here, the transition path and energetics for double cross-slip of pyramidal I 〈 c + a 〉 dislocations are analysed in the regime where the pyramidal I dislocation is energetically more favorable than the pyramidal II. This is achieved using nudged elastic band simulations on a proxy MEAM potential for Mg designed to favor the pyramidal I over pyramidal II. The minimum energy transition path for pyramidal I double cross-slip is found to initiate with cross-slip onto a pyramidal II plane followed by cross-slip onto a pyramidal I plane parallel to the original pyramidal I plane. A previous mechanistic model for ductility is then extended to higher solute concentrations where pyramidal I is favorable. The model predicts an upper limit of solute concentrations beyond which ductility again becomes poor in Mg alloys. The model predictions are consistent with limited experiments of Mg-RE alloys at high concentrations and motivate further experimental studies in the high concentration regime.
The thermally activated pyramidal-to-basal (PB) transition of c + a dislocations, transforming gl... more The thermally activated pyramidal-to-basal (PB) transition of c + a dislocations, transforming glissile pyramidal dissociated core structures into sessile basal dissociated ones, lies at the origin of low ductility in pure magnesium (Mg). Solute-accelerated cross-slip and double cross-slip of pyramidal c + a dislocations have recently been proposed as a mechanism that can circumvent the deleterious effects of the PB transition by enabling rapid dislocation multiplication and isolating PB-transformed sessile segments. Here, the theory for solute-accelerated cross-slip is revisited with an explicit atomistic derivation, is extended to include multiple very dilute solute concentrations, and various aspects of the theory are demonstrated computationally. DFT inputs to the theory for a wide range of new alloying elements are presented. The theory is validated by comparing predicted ductility to literature experiments for a range of alloys. The theory is then applied to predict composition ranges for ductility in rare-earth free ternary and quaternary dilute alloys. The wide range of new alloys predicted to be ductile can serve as a guide to experimental development of new ductile Mg alloys.
Modelling and Simulation in Materials Science and Engineering, Jul 20, 2018
An interatomic potential for the Mg–Y binary system is developed within the framework of the seco... more An interatomic potential for the Mg–Y binary system is developed within the framework of the second-nearest-neighbor modified embedded-atom method (MEAM) based on a very good MEAM potential for pure Mg. The Mg–Y potential is fitted to a range of key physical properties, either experimental or computed by first-principles methods, including the Y interaction energy with basal and pyramidal stacking faults, and properties of the B2 Mg–Y intermetallic phase. Reasonable agreement is obtained—much better than existing potentials in the literature—but differences remain for subtle but important aspects of Y solutes in Mg. The predictions of the potential for Y misfit volume in Mg, Y solute interactions with the pyramidal II (c + a) edge dislocation and {1012} tension-twin boundary are then compared against recent density functional theory results, and reasonable accuracy is obtained. In light of the spectrum of results presented here, the applicability and limitations of this Mg–Y MEAM potential for investigating various plasticity phenomena in Mg–Y solid solution alloys are carefully discussed.
The Mg–Y binary system's potential has been developed within the framework of the second-near... more The Mg–Y binary system's potential has been developed within the framework of the second-nearest-neighbor modified embedded-atom method (MEAM) based on a potential for pure Mg. The potential fitting is done on a range of physical properties, either experimental or computed by first-principles methods. These include the Y interaction energy with basal and pyramidal stacking faults and properties of the B2 Mg–Y intermetallic phase. Using this model, one can make predictions generally in reasonable agreement with experiments and or DFT, but differences remain for subtle but important aspects of Y solutes in Mg.
Abstract Solute accelerated cross-slip of pyramidal 〈 c + a 〉 screw dislocations has recently bee... more Abstract Solute accelerated cross-slip of pyramidal 〈 c + a 〉 screw dislocations has recently been recognized as a crucial mechanism in enhancing the ductility of solid-solution Mg alloys. In pure Mg, cross-slip is ineffective owing to the energy difference between the high energy pyramidal I and low energy pyramidal II 〈 c + a 〉 screw dislocations. A small addition of solutes, especially rare earth (RE) elements, can reduce this energy difference and accelerate cross-slip, thus enabling enhanced ductility. With increasing solute concentrations, the pyramidal I dislocation can become energetically favorable, which switches the primary 〈 c + a 〉 slip plane and alters the cross-slip process. Here, the transition path and energetics for double cross-slip of pyramidal I 〈 c + a 〉 dislocations are analysed in the regime where the pyramidal I dislocation is energetically more favorable than the pyramidal II. This is achieved using nudged elastic band simulations on a proxy MEAM potential for Mg designed to favor the pyramidal I over pyramidal II. The minimum energy transition path for pyramidal I double cross-slip is found to initiate with cross-slip onto a pyramidal II plane followed by cross-slip onto a pyramidal I plane parallel to the original pyramidal I plane. A previous mechanistic model for ductility is then extended to higher solute concentrations where pyramidal I is favorable. The model predicts an upper limit of solute concentrations beyond which ductility again becomes poor in Mg alloys. The model predictions are consistent with limited experiments of Mg-RE alloys at high concentrations and motivate further experimental studies in the high concentration regime.
The thermally activated pyramidal-to-basal (PB) transition of c + a dislocations, transforming gl... more The thermally activated pyramidal-to-basal (PB) transition of c + a dislocations, transforming glissile pyramidal dissociated core structures into sessile basal dissociated ones, lies at the origin of low ductility in pure magnesium (Mg). Solute-accelerated cross-slip and double cross-slip of pyramidal c + a dislocations have recently been proposed as a mechanism that can circumvent the deleterious effects of the PB transition by enabling rapid dislocation multiplication and isolating PB-transformed sessile segments. Here, the theory for solute-accelerated cross-slip is revisited with an explicit atomistic derivation, is extended to include multiple very dilute solute concentrations, and various aspects of the theory are demonstrated computationally. DFT inputs to the theory for a wide range of new alloying elements are presented. The theory is validated by comparing predicted ductility to literature experiments for a range of alloys. The theory is then applied to predict composition ranges for ductility in rare-earth free ternary and quaternary dilute alloys. The wide range of new alloys predicted to be ductile can serve as a guide to experimental development of new ductile Mg alloys.
International Journal of Multicultural and Multireligious Understanding, 2020
This study aimed to examine the effect of strategic planning that is represented by three dimensi... more This study aimed to examine the effect of strategic planning that is represented by three dimensions (top management support, technology and strategic goals) on entrepreneurship strategy requirements that is represented by three dimensions (creative capabilities, risk taking,and entrepreneurial culture) related to the perspectives of employees at private hospitals in Iraqi Kurdistan Region Erbil city. A survey questionnaire has been used to collect data, and the questionnaires distributed randomly to (150) health staff comprising of a number of private hospitals, (146) of staff were able to fill and return the questionnaires however (142) of the questionnaires was suitable for the purpose of statically analyzing. The questionnaire encompassed two sections with 30 closed-ended questions. Data collected analyzed quantitatively by using SPSS program version 20. The results of study concluded that there is a moderate and positive correlation as well as a significant impact of strategic ...
Effective permeability is a key physical property of porous media that defines its ability to tra... more Effective permeability is a key physical property of porous media that defines its ability to transport fluid. Digital rock physics combines modern tomographic imaging techniques with advanced numerical simulations to estimate effective rock properties. Digital rock physics is used to complement or replace expensive and time-consuming or impractical laboratory measurements. However, with increase in sample size to capture multimodal and multiscale microstructures, conventional approaches based on direct numerical simulation (DNS) are becoming very computationally intensive or even infeasible. To address this computational challenge, we propose a hierarchical homogenization method (HHM) with a data-driven surrogate model based on 3-D convolutional neural network (CNN) and transfer learning to estimate effective permeability of digital rocks with large sample sizes up to billions of voxels. This workflow (HHM-CNN) divides the large digital rock into small sub-volumes and predicts the sub-volume permeabilities through a CNN surrogate model of Stokes flow at the pore scale. The effective permeability of the full digital rock is then predicted by solving the Darcy equations efficiently on the upscaled model in which the permeability of each cell is assigned by the surrogate model. The proposed method is verified on micro-CT data of both sandstones and carbonates as well as the reconstructed high-resolution digital rock obtained by multiscale data fusion. The computed permeabilities of our proposed hierarchical approach are consistent with the results of the DNS on the full digital rock. Compared with conventional DNS algorithms, the proposed hierarchical approach can largely reduce the computational time and memory demand.
Modelling and Simulation in Materials Science and Engineering, Jul 22, 2022
Taking advantage of the advances in generative deep learning, particularly normalizing flow, a fr... more Taking advantage of the advances in generative deep learning, particularly normalizing flow, a framework, called Boltzmann Generator, has recently been proposed for the purpose of generating equilibrium atomic configurations from the canonical ensemble and determining the associated free energy. In this work, we revisit Boltzmann Generator to motivate the construction of the loss function from the statistical mechanical point of view, and to cast the training of the neural networks in purely unsupervised manner that requires no samples of the atomic configurations from the equilibrium ensemble. We further show that the normalizing flow framework furnishes a reference thermodynamic system, very close to the real thermodynamic system under consideration, that is suitable for the well-established free energy perturbation methods to determine accurate free energy of solids. We then apply the normalizing flow to two problems: temperature-dependent Gibbs free energy of perfect crystal and formation free energy of monovacancy defect in a model system of diamond cubic Si. The results obtained from the normalizing flow are shown to be in good agreement with that obtained from independent well-established free energy methods.
Taking advantage of the advances in generative deep learning, particularly normalizing flow, a fr... more Taking advantage of the advances in generative deep learning, particularly normalizing flow, a framework, called Boltzmann Generator, has recently been proposed for the purpose of generating equilibrium atomic configurations from the canonical ensemble and determining the associated free energy. In this work, we revisit Boltzmann Generator to motivate the construction of the loss function from the statistical mechanical point of view, and to cast the training of the neural networks in purely unsupervised manner that requires no samples of the atomic configurations from the equilibrium ensemble. We further show that the normalizing flow framework furnishes a reference thermodynamic system, very close to the real thermodynamic system under consideration, that is suitable for the well-established free energy perturbation methods to determine accurate free energy of solids. We then apply the normalizing flow to two problems: temperature-dependent Gibbs free energy of perfect crystal and formation free energy of monovacancy defect in a model system of diamond cubic Si. The results obtained from the normalizing flow are shown to be in good agreement with that obtained from independent well-established free energy methods.
Determining effective elastic properties of rocks from their pore-scale digital images is a key g... more Determining effective elastic properties of rocks from their pore-scale digital images is a key goal of digital rock physics (DRP). Direct numerical simulation (DNS) of elastic behavior, however, incurs high computational cost; and surrogate machine learning (ML) model, particularly convolutional neural network (CNN), show promises to accelerate homogenization process. 3D CNN models, however, are unable to handle large images due to memory issues. To address this challenge, we propose a novel method that combines 3D CNN with hierarchical homogenization method (HHM). The surrogate 3D CNN model homogenizes only small subimages, and a DNS is used to homogenize the intermediate image obtained by assembling small subimages. The 3D CNN model is designed to output the homogenized elastic constants within the Hashin-Shtrikman (HS) bounds of the input images. The 3D CNN model is first trained on data comprising equal proportions of five sandstone (quartz mineralogy) images, and, subsequently, fine-tuned for specific rocks using transfer learning. The proposed method is applied to homogenize the rock images of size 300×300×300 and 600×600×600 voxels, and the predicted homogenized elastic moduli are shown to agree with that obtained from the brute-force DNS. The transferability of the trained 3D CNN model (using transfer learning) is further demonstrated by predicting the homogenized elastic moduli of a limestone rock with calcite mineralogy. The surrogate 3D CNN model in combination with the HHM is thus shown to be a promising tool for the homogenization of large 3D digital rock images and other random media.
This work focuses on computing the homogenized elastic properties of rocks from 3D micro-computed... more This work focuses on computing the homogenized elastic properties of rocks from 3D micro-computed-tomography (micro-CT) scanned images. The accurate computation of homogenized properties of rocks, archetypal random media, requires both resolution of intricate underlying microstructure and large field of view, resulting in huge micro-CT images. Homogenization entails solving the local elasticity problem computationally which can be prohibitively expensive for a huge image. To mitigate this problem, we use a renormalization method inspired scheme, the hierarchical homogenization method, where a large image is partitioned into smaller subimages. The individual subimages are separately homogenized using periodic boundary conditions, and then assembled into a much smaller intermediate image. The intermediate image is again homogenized, subject to the periodic boundary condition, to find the final homogenized elastic constant of the original image. An FFT-based elasticity solver is used to solve the associated periodic elasticity problem. The error in the homogenized elastic constant is empirically shown to follow a power law scaling with exponent −1 with respect to the subimage size across all five microstructures of rocks. We further show that the inclusion of surrounding materials during the homogenization of the small subimages reduces error in the final homogenized elastic moduli while still respecting the power law with the exponent of −1. This power law scaling is then exploited to determine a better approximation of the large heterogeneous microstructures based on Richardson extrapolation.
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Papers by Dr.Rasool Ahmad