5,623 research outputs found

    Capturing Nucleation at 4D Atomic Resolution

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    Nucleation plays a critical role in many physical and biological phenomena ranging from crystallization, melting and evaporation to the formation of clouds and the initiation of neurodegenerative diseases. However, nucleation is a challenging process to study in experiments especially in the early stage when several atoms/molecules start to form a new phase from its parent phase. Here, we advance atomic electron tomography to study early stage nucleation at 4D atomic resolution. Using FePt nanoparticles as a model system, we reveal that early stage nuclei are irregularly shaped, each has a core of one to few atoms with the maximum order parameter, and the order parameter gradient points from the core to the boundary of the nucleus. We capture the structure and dynamics of the same nuclei undergoing growth, fluctuation, dissolution, merging and/or division, which are regulated by the order parameter distribution and its gradient. These experimental observations differ from classical nucleation theory (CNT) and to explain them we propose the order parameter gradient (OPG) model. We show the OPG model generalizes CNT and energetically favours diffuse interfaces for small nuclei and sharp interfaces for large nuclei. We further corroborate this model using molecular dynamics simulations of heterogeneous and homogeneous nucleation in liquid-solid phase transitions of Pt. We anticipate that the OPG model is applicable to different nucleation processes and our experimental method opens the door to study the structure and dynamics of materials with 4D atomic resolution.Comment: 42 pages, 5 figures, 12 supplementary figures and one supplementary tabl

    Understanding Homogeneous Nucleation in Solidification of Aluminum by Molecular Dynamics Simulations

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    Homogeneous nucleation from aluminum (Al) melt was investigated by million-atom molecular dynamics (MD) simulations utilizing the second nearest neighbor modified embedded atom method (MEAM) potentials. The natural spontaneous homogenous nucleation from the Al melt was produced without any influence of pressure, free surface effects and impurities. Initially isothermal crystal nucleation from undercooled melt was studied at different constant temperatures, and later superheated Al melt was quenched with different cooling rates. The crystal structure of nuclei, critical nucleus size, critical temperature for homogenous nucleation, induction time, and nucleation rate were determined. The quenching simulations clearly revealed three temperature regimes: sub-critical nucleation, super-critical nucleation, and solid-state grain growth regimes. The main crystalline phase was identified as face-centered cubic (fcc), but a hexagonal close-packed (hcp) and an amorphous solid phase were also detected. The hcp phase was created due to the formation of stacking faults during solidification of Al melt. By slowing down the cooling rate, the volume fraction of hcp and amorphous phases decreased. After the box was completely solid, grain growth was simulated and the grain growth exponent was determined for different annealing temperatures.Comment: 41 page

    Extended Ensemble Monte Carlo

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    ``Extended Ensemble Monte Carlo''is a generic term that indicates a set of algorithms which are now popular in a variety of fields in physics and statistical information processing. Exchange Monte Carlo (Metropolis-Coupled Chain, Parallel Tempering), Simulated Tempering (Expanded Ensemble Monte Carlo), and Multicanonical Monte Carlo (Adaptive Umbrella Sampling) are typical members of this family. Here we give a cross-disciplinary survey of these algorithms with special emphasis on the great flexibility of the underlying idea. In Sec.2, we discuss the background of Extended Ensemble Monte Carlo. In Sec.3, 4 and 5, three types of the algorithms, i.e., Exchange Monte Carlo, Simulated Tempering, Multicanonical Monte Carlo are introduced. In Sec.6, we give an introduction to Replica Monte Carlo algorithm by Swendsen and Wang. Strategies for the construction of special-purpose extended ensembles are discussed in Sec.7. We stress that an extension is not necessary restricted to the space of energy or temperature. Even unphysical (unrealizable) configurations can be included in the ensemble, if the resultant fast mixing of the Markov chain offsets the increasing cost of the sampling procedure. Multivariate (multi-component) extensions are also useful in many examples. In Sec.8, we give a survey on extended ensembles with a state space whose dimensionality is dynamically varying. In the appendix, we discuss advantages and disadvantages of three types of extended ensemble algorithms.Comment: Major revision that includes addition of concrete examples, references, improved introduction to Multicanonical MC, change in the order of the sections, and a number of small but important corrections. 49 pages, no figure

    Modeling Complex Material Systems Using Stochastic Reconstruction and Lattice Particle Simulation

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    abstract: In this dissertation, three complex material systems including a novel class of hyperuniform composite materials, cellularized collagen gel and low melting point alloy (LMPA) composite are investigated, using statistical pattern characterization, stochastic microstructure reconstruction and micromechanical analysis. In Chapter 1, an introduction of this report is provided, in which a brief review is made about these three material systems. In Chapter 2, detailed discussion of the statistical morphological descriptors and a stochastic optimization approach for microstructure reconstruction is presented. In Chapter 3, the lattice particle method for micromechanical analysis of complex heterogeneous materials is introduced. In Chapter 4, a new class of hyperuniform heterogeneous material with superior mechanical properties is investigated. In Chapter 5, a bio-material system, i.e., cellularized collagen gel is modeled using correlation functions and stochastic reconstruction to study the collective dynamic behavior of the embed tumor cells. In chapter 6, LMPA soft robotic system is generated by generalizing the correlation functions and the rigidity tunability of this smart composite is discussed. In Chapter 7, a future work plan is presented.Dissertation/ThesisDoctoral Dissertation Materials Science and Engineering 201

    A Hybrid of Ant Colony Optimization Algorithm and Simulated Annealing for Classification Rules

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    Ant colony optimization (ACO) is a metaheuristic approach inspired from the behaviour of natural ants and can be used to solve a variety of combinatorial optimization problems. Classification rule induction is one of the problems solved by the Ant-miner algorithm, a variant of ACO, which was initiated by Parpinelli in 2001. Previous studies have shown that ACO is a promising machine learning technique to generate classification rules. However, the Ant-miner is less class focused since the rule’s class is assigned after the rule was constructed. There is also the case where the Ant-miner cannot find any optimal solution for some data sets. Thus, this thesis proposed two variants of hybrid ACO with simulated annealing (SA) algorithm for solving problem of classification rule induction. In the first proposed algorithm, SA is used to optimize the rule's discovery activity by an ant. Benchmark data sets from various fields were used to test the proposed algorithms. Experimental results obtained from this proposed algorithm are comparable to the results of the Ant-miner and other well-known rule induction algorithms in terms of rule accuracy, but are better in terms of rule simplicity. The second proposed algorithm uses SA to optimize the terms selection while constructing a rule. The algorithm fixes the class before rule's construction. Since the algorithm fixed the class before each rule's construction, a much simpler heuristic and fitness function is proposed. Experimental results obtained from the proposed algorithm are much higher than other compared algorithms, in terms of predictive accuracy. The successful work on hybridization of ACO and SA algorithms has led to the improved learning ability of ACO for classification. Thus, a higher predictive power classification model for various fields could be generated

    Coupled Al/Si and O/N order/disorder in BaYb[Si4–xAlxOxN7–x]sialon

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    The fractions of aluminium, [Al]/[Al + Si], and oxygen, [O]/[O + N], in crystallographically distinct sites of BaYb[Si4–xAlxOxN7–x] oxonitridoaluminosilicate (space group P63mc, No. 186) were refined based on the results of neutron powder diffraction for a synthetic sample with the composition of x = 2.2(2) and simulated as functions of temperature for the compositions x = 2 and x = 2.3 using a combination of static lattice energy calculations (SLEC) and Monte Carlo simulations. The SLEC calcu lations have been performed on a set of 800 structures differing in the distribution of Al/Si and O/N within the 2 × 2 × 2 supercell containing 36 formula units of BaYb[Si4–xAlxOxN7–x]. The SLEC were based on a transferable set of empirical interatomic potentials developed within the present study. The static lattice energies of these structures have been expanded in the basis set of pair-wise ordering energies and on-site chemical potentials. The ordering energies and the chemical potentials have been used to calculate the configuration energies of the oxonitridoaluminosilicates (so-called sialons) using a Monte Carlo algorithm. The simulations suggest that Al and O are distributed unevenly over two non-equivalent T(Si/Al) and three L(N/O) sites, respectively, and the distribution shows strong dependence both on the temperature and the composition. Both simulated samples exhibit order/disorder transitions in the temperature range 500–1000 K to phases with partial long-range order below these temperatures. Above the transition temperatures the Si/Al and N/O distributions are affected by short-range ordering. The predicted site occupancies are in a qualitative agreement with the neutron diffraction results

    Diversification and Intensification in Hybrid Metaheuristics for Constraint Satisfaction Problems

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    Metaheuristics are used to find feasible solutions to hard Combinatorial Optimization Problems (COPs). Constraint Satisfaction Problems (CSPs) may be formulated as COPs, where the objective is to reduce the number of violated constraints to zero. The popular puzzle Sudoku is an NP-complete problem that has been used to study the effectiveness of metaheuristics in solving CSPs. Applying the Simulated Annealing (SA) metaheuristic to Sudoku has been shown to be a successful method to solve CSPs. However, the ‘easy-hard-easy’ phase-transition behavior frequently attributed to a certain class of CSPs makes finding a solution extremely difficult in the hard phase because of the vast search space, the small number of solutions and a fitness landscape marked by many plateaus and local minima. Two key mechanisms that metaheuristics employ for searching are diversification and intensification. Diversification is the method of identifying diverse promising regions of the search space and is achieved through the process of heating/reheating. Intensification is the method of finding a solution in one of these promising regions and is achieved through the process of cooling. The hard phase area of the search terrain makes traversal without becoming trapped very challenging. Running the best available method - a Constraint Propagation/Depth-First Search algorithm - against 30,000 benchmark problem-instances, 20,240 remain unsolved after ten runs at one minute per run which we classify as very hard. This dissertation studies the delicate balance between diversification and intensification in the search process and offers a hybrid SA algorithm to solve very hard instances. The algorithm presents (a) a heating/reheating strategy that incorporates the lowest solution cost for diversification; (b) a more complex two-stage cooling schedule for faster intensification; (c) Constraint Programming (CP) hybridization to reduce the search space and to escape a local minimum; (d) a three-way swap, secondary neighborhood operator for a low expense method of diversification. These techniques are tested individually and in hybrid combinations for a total of 11 strategies, and the effectiveness of each is evaluated by percentage solved and average best run-time to solution. In the final analysis, all strategies are an improvement on current methods, but the most remarkable results come from the application of the “Quick Reset” technique between cooling stages
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