A biased Monte Carlo scheme for zeolite structure solution

Falcioni, M.; Deem, Michael W.

doi:10.1063/1.477812

Cited by 280 publications

(227 citation statements)

References 34 publications

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“…The sampling of configuration space is enhanced by using "jump moves", i.e. large amplitude trial displacements for the cations, and by simulating the system at 700 K. While this does not correspond to the target temperature, we observed that it provides in the cases under consideration the same results as parallel tempering [5,17] at a much lower computational cost (only one temperature is used, instead of the 14 replicas for our parallel tempering simulations). In particular, we have checked that this procedure allows us to obtain reproducible results starting from uncorrelated initial conditions, and to overcome possible free energy barriers separating metastable cation distributions, which may result in hysteresis in the hydration/dehydration processes.…”

Section: Monte Carlo Simulationsmentioning

confidence: 58%

Cation redistribution upon dehydration of Na₅₈Y faujasite zeolite: a joint neutron diffraction and molecular simulation study

Louisfrema

Rotenberg

Porcher

et al. 2015

Molecular Simulation

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Using neutron scattering and Monte Carlo simulation, we investigate the distribution of cations in Na 58 Y faujasite upon (de)hydration. We introduce a new method for the assignment of cations to specific sites in molecular simulations from their local environment. This allows us to bypass the need of the coordinates of crystallographic sites, which vary as water adsorption induces changes in the zeolite framework structure. While the agreement between experiments and simulation is excellent at high temperature, some differences are observed below 150°C. We show that these differences are due to the presence of water and that temperature itself as well as adsorption-induced deformation of the framework play a less important role. We demonstrate the migration of sodium to sites III upon water adsorption, not observed for other Si:Al ratios.

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Section: Monte Carlo Simulationsmentioning

confidence: 58%

Cation redistribution upon dehydration of Na₅₈Y faujasite zeolite: a joint neutron diffraction and molecular simulation study

Louisfrema

Rotenberg

Porcher

et al. 2015

Molecular Simulation

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“…20, except for the use of parallel tempering [33][34][35][36][37][38][39] to cope with possible ergodicity problems. We have utilized a number of 42 parallel streams, each running a replica of the system at a different temperature.…”

Section: A Sampling Strategymentioning

confidence: 99%

“…[33][34][35][36][37][38][39] Any given stream attempts a swap with the neighboring streams of lower and higher temperatures in succession. Because of this swapping strategy, the streams of minimum and maximum temperatures are involved in swaps every 50 steps, only.…”

Section: A Sampling Strategymentioning

confidence: 99%

Heat capacity estimators for random series path-integral methods by finite-difference schemes

Predescu

Sabo

Doll

et al. 2003

The Journal of Chemical Physics

101

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Previous heat capacity estimators used in path integral simulations either have large variances that grow to infinity with the number of path variables or require the evaluation of first-and second-order derivatives of the potential. In the present paper, we show that the evaluation of the total energy by the T-method estimator and of the heat capacity by the TT-method estimator can be implemented by a finite difference scheme in a stable fashion. As such, the variances of the resulting estimators are finite and the evaluation of the estimators requires the potential function only. By comparison with the task of computing the partition function, the evaluation of the estimators requires kϩ1 times more calls to the potential, where k is the order of the difference scheme employed. Quantum Monte Carlo simulations for the Ne 13 cluster demonstrate that a second order central-difference scheme should suffice for most applications.

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“…The first known algorithm for developing MOF structures was presented in 2000 74 , the primary purpose of which was to predict new structures rather than construct databases of MOF structures. The method, titled Automatic Assembly of Secondary Building Units (AASBU), borrowed ideas from zeolite and bulk material prediction algorithms [75][76][77] . Namely, at the core of the AASBU method is a global optimization technique, where MOF building blocks (SBUs) are treated as rigid units containing 'sticky' atoms, and as these building blocks are randomly perturbed in a large simulation box as a function of temperature, nearby sticky atoms will adhere and break to dictate the formation of extended coordination polymers 73 .…”

Section: H1 Database Development and The Quest For Diversitymentioning

confidence: 99%

“…How each class of material performs for a particular application is largely dependent on their physical pore characteristics, a simplified measure of which are presented in Figure 1. [75][76][77] . Namely, at the core of the AASBU method is a global optimization technique, where MOF building blocks (SBUs) are treated as rigid units containing 'sticky' atoms, and as these building blocks are randomly perturbed in a large simulation box as a function of temperature, nearby sticky atoms will adhere and break to dictate the formation of extended coordination polymers 73 .…”

mentioning

confidence: 99%

Computational development of the nanoporous materials genome

2017

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H1 Abstract.There is currently a large push towards big data and data mining in materials research to accelerate discovery. Zeolites, metal-organic frameworks (MOFs) and other related crystalline porous materials are not immune to this recent phenomenon, as evidenced by the proliferation of porous structure databases and computational gas adsorption screening studies over the past decade. The strive to identify the best materials for a variety of gas separation and storage applications has not only led to collections of thousands of synthesised structures, but the development of hypothetical material building algorithms.The materials databases assembled with these algorithms expand greatly on the range of complex pore structures that have been synthesised, with the rationale that we have discovered only a small fraction of realisable structures and expanding upon these will accelerate rational design. In this review, we highlight some of the methods developed to build these databases, and some of the important outcomes resulting from large-scale computational screening efforts. SummaryIn the field of nanoporous materials discovery, we are witnessing the emergence of big data analytics combined with traditional computational thermodynamics calculations. This review turns a critical eye on the current state of the art, with a focus on computational database generation and results from large-scale screening for gas separations. H1 Introduction.The discovery, in the late 19 th century, that zeolites could trap interesting and valuable particles in their pores opened up an entire field of research 1,2 . This seemingly simple phenomenon was an enormous boon to the oil and gas industry, seeing as how cheap, but effective porous materials could serve as a catalyst for hydrocarbon cracking. From their humble beginnings (being carved out of rock faces), zeolites, and their ability to selectively trap guests in their pores, have become integral in not only the oil and gas industry, but in detergents (as ion exchangers) and natural gas purification to name a few 1 .Zeolites have since enjoyed an almost exclusive dominance in porous materials research until the late 20 th century, when we began to observe the creation of more diverse materials in terms of chemistry, network connectivity, and physical properties.At present, there are a little over 200 known zeolite structures, which is only a small fraction of the total number of structures that have been predicted 3 . In addition, if we were also able to expand the chemical diversity of these materials beyond the conventional Si 4+ , O 2-, and Al 3+ ions found in zeolites, one could envision designing materials for virtually any gas separation application. Thus when the first articles arose in the 1990's characterising Metal-Organic Frameworks (MOFs) [4][5][6][7][8] , and later Covalent Organic Frameworks (COFs) 9,10 , Zeolitic Imidazolate Frameworks (ZIFs) 11 , and Porous Polymer Networks (PPNs) 12 the excitement was palpable. It was recognised early-on by Yaghi, O'Keeffe, ...

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A biased Monte Carlo scheme for zeolite structure solution

Cited by 280 publications

References 34 publications

Cation redistribution upon dehydration of Na₅₈Y faujasite zeolite: a joint neutron diffraction and molecular simulation study

Cation redistribution upon dehydration of Na₅₈Y faujasite zeolite: a joint neutron diffraction and molecular simulation study

Heat capacity estimators for random series path-integral methods by finite-difference schemes

Computational development of the nanoporous materials genome

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A biased Monte Carlo scheme for zeolite structure solution

Cited by 280 publications

References 34 publications

Cation redistribution upon dehydration of Na58Y faujasite zeolite: a joint neutron diffraction and molecular simulation study

Cation redistribution upon dehydration of Na58Y faujasite zeolite: a joint neutron diffraction and molecular simulation study

Heat capacity estimators for random series path-integral methods by finite-difference schemes

Computational development of the nanoporous materials genome

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Cation redistribution upon dehydration of Na₅₈Y faujasite zeolite: a joint neutron diffraction and molecular simulation study

Cation redistribution upon dehydration of Na₅₈Y faujasite zeolite: a joint neutron diffraction and molecular simulation study