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Record Nr. |
UNINA9910254604203321 |
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Autore |
Baldock Robert John Nicholas |
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Titolo |
Classical Statistical Mechanics with Nested Sampling / / by Robert John Nicholas Baldock |
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Pubbl/distr/stampa |
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Cham : , : Springer International Publishing : , : Imprint : Springer, , 2017 |
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ISBN |
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Edizione |
[1st ed. 2017.] |
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Descrizione fisica |
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1 online resource (XII, 144 p. 30 illus., 25 illus. in color.) |
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Collana |
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Springer Theses, Recognizing Outstanding Ph.D. Research, , 2190-5053 |
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Disciplina |
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Soggetti |
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Statistical physics |
Dynamics |
Phase transformations (Statistical physics) |
Physics |
Complex Systems |
Phase Transitions and Multiphase Systems |
Numerical and Computational Physics, Simulation |
Statistical Physics and Dynamical Systems |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di bibliografia |
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Includes bibliographical references at the end of each chapters. |
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Nota di contenuto |
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Introduction -- A Primer in Probability -- Phase Space Probability Distributions for Various External Conditions -- Relating Probability Density Functions to the Behaviour of Systems -- The Strategy of Nested Sampling -- Nested Sampling for Materials -- Equations of State -- Parallelising Nested Sampling -- Hamiltonian Monte Carlo for the Canonical Distribution -- Hamiltonian Monte Carlo for Nested Sampling -- Conclusion of Thesis and Further Work. |
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Sommario/riassunto |
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This thesis develops a nested sampling algorithm into a black box tool for directly calculating the partition function, and thus the complete phase diagram of a material, from the interatomic potential energy function. It represents a significant step forward in our ability to accurately describe the finite temperature properties of materials. In principle, the macroscopic phases of matter are related to the microscopic interactions of atoms by statistical mechanics and the |
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