Cuckoo searching optimal composition of multicomponent alloys by molecular simulations

Sharma, Akshay; Singh, Rahul; Liaw, Peter K.; Balasubramanian, Ganesh

doi:10.1016/j.scriptamat.2016.12.022

Cited by 25 publications

(5 citation statements)

References 47 publications

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“…Predicting long‐range ordering in HEOs may require approaches that combine DFT with Monte Carlo or MD techniques, 37 or with cluster expansion methods 58 . Recent development of evolutionary algorithms, including the cuckoo search, 127 the hybrid cuckoo search, 128 and genetic algorithms, 68 may help automate computational analysis of high‐entropy materials. Notably, machine‐learning methods including deep learning 129 have been employed to obtain MD potentials from first‐principles datasets for high‐entropy carbides and may ultimately be of immense utility for modeling HEOs.…”

Section: Computational Considerationsmentioning

confidence: 99%

High‐entropy oxides: Harnessing crystalline disorder for emergent functionality

et al. 2023

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High-entropy materials defy historical materials design paradigms by leveraging chemical disorder to kinetically stabilize novel crystalline solid solutions comprised of many end-members. Formulational diversity results in local crystal structures that are seldom found in conventional materials and can strongly influence macroscopic physical properties. Thermodynamically prescribed chemical flexibility provides a means to tune such properties. Additionally, kinetic metastability results in many possible atomic arrangements, including both solid-solution configurations and heterogeneous phase assemblies, depending on synthesis conditions. Local disorder induced by metastability, and extensive cation solubilities allowed by thermodynamics combine to give many high-entropy oxide systems utility as electrochemical, magnetic, thermal, dielectric, and optical materials. Though high-entropy materials research is maturing rapidly, much remains to be understood and many compositions still await discovery, exploration, and implementation.

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Section: Computational Considerationsmentioning

confidence: 99%

High‐entropy oxides: Harnessing crystalline disorder for emergent functionality

et al. 2023

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“…3. Boundary issue CS algorithm uses Lévy flights and random walk to find nest location [18,30]. The locations of some nests may be out of the boundary; when this happens CS algorithm uses the boundary value to replace these location.…”

Section: Parameters α and P Amentioning

confidence: 99%

Modified Cuckoo Search Algorithm with Variational Parameters and Logistic Map

Liu

Wang

et al. 2018

Algorithms

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Cuckoo Search (CS) is a Meta-heuristic method, which exhibits several advantages such as easier to application and fewer tuning parameters. However, it has proven to very easily fall into local optimal solutions and has a slow rate of convergence. Therefore, we propose Modified cuckoo search algorithm with variational parameter and logistic map (VLCS) to ameliorate these defects. To balance the exploitation and exploration of the VLCS algorithm, we not only use the coefficient function to change step size α and probability of detection p a at next generation, but also use logistic map of each dimension to initialize host nest location and update the location of host nest beyond the boundary. With fifteen benchmark functions, the simulations demonstrate that the VLCS algorithm can over come the disadvantages of the CS algorithm.In addition, the VLCS algorithm is good at dealing with high dimension problems and low dimension problems.

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“…Inspired by successes, 18,19,26 including materials design, 27 our hybrid-CS schema is more efficient than CS, as we establish for standard test functions, where CS already bests all other evolutionary algorithms. Our hybrid CS employs Lévy flights for global optimization and Monte Carlo (MC) for local explorations of large multimodal design space.…”

mentioning

confidence: 94%

Accelerating computational modeling and design of high-entropy alloys

Singh,

Sharma,

Singh

et al. 2020

Preprint

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With huge design spaces for unique chemical and mechanical properties, we remove a roadblock to computational design of high-entropy alloys using a metaheuristic hybrid Cuckoo Search (CS) for "on-the-fly" construction of Super-Cell Random APproximates (SCRAPs) having targeted atomic site and pair probabilities on arbitrary crystal lattices. Our hybrid-CS schema overcomes large, discrete combinatorial optimization by ultrafast global solutions that scale linearly in system size and strongly in parallel, e.g. a 4-element, 128-atom model [a 10 73+ space] is found in seconds -a reduction of 13,000+ over current strategies. With model-generation eliminated as a bottleneck, computational alloy design can be performed that is currently impossible or impractical. We showcase the method for real alloys with varying short-range order. Being problem-agnostic, our hybrid-CS schema offers numerous applications in diverse fields. I. INTRODUCTIONComplex solid-solution alloys (CSAs), a subset of which are near-equiatomic high-entropy alloys, 1-7 show remarkable properties for number of elements N ≥ 4 and set of elemental compositions {c α=1,N }, 8 even for mediumentropy (N =3). These findings have encouraged research into CSAs for applications in aerospace and defense, like adding refractory elements for higher operational temperatures. For higher-melting refractory CSAs, vacancy defects -ubiquitous when processing -can have a profound influence on stability and phase selection 9 , adding another design "element". Thus, CSAs have a vast design space to create materials with novel or improved properties (e.g., structural strength or resistance to fatigue, oxidation, corrosion, and wear), while other properties (e.g., resistivity, thermoelectricity, elasticity, and yield strength) can alter rapidly with a set of {c α }. [6][7][8][9][10][11][12][13][14][15] As such, accurate "on-the-fly" constructed CSA models are needed to enable computational design and to identify trends in {c α }-derived properties and thermal stability. Yet, models of CSAs have a design space that grows exponentially -a NP-hard combinatorial problem.To remove this design roadblock, we establish a hybrid Cuckoo Search (CS), an evolutionary algorithm 16 inspired by Yang and Deb 17 based on the brood parasitism of a female Cuckoo bird (mimicking color and pattern of a few host species). Importantly, the CS advantages are: (a) guaranteed global convergence, (b) local and global searches controlled by a switching parameter, and (c) Lévy flights scan solution space more efficiently -no random walks, so better than a Gaussian process. 17-19 A CS yields approximate solutions ("nests") for intractable or gradient-free problems 20 with little problem-specific knowledge -often only a solution "fitness" function. 21 For complex cases, fitness can be discontinuous, non-differentiable to noisy. Related methods 18 are genetic-algorithm, 22 simulated-annealing, 23 particleswarm, 24 and ant-colony 25 optimization.

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Cuckoo searching optimal composition of multicomponent alloys by molecular simulations

Cited by 25 publications

References 47 publications

High‐entropy oxides: Harnessing crystalline disorder for emergent functionality

High‐entropy oxides: Harnessing crystalline disorder for emergent functionality

Modified Cuckoo Search Algorithm with Variational Parameters and Logistic Map

Accelerating computational modeling and design of high-entropy alloys

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