June Gunn Lee's Computational Materials Science: An Introduction, Second PDF

By June Gunn Lee

ISBN-10: 1498749739

ISBN-13: 9781498749732

This publication covers the necessities of Computational technological know-how and provides instruments and strategies to resolve fabrics technology difficulties utilizing molecular dynamics (MD) and first-principles equipment. the recent variation expands upon the density useful idea (DFT) and the way the unique DFT has complicated to a extra actual point by means of GGA+U and hybrid-functional tools. It deals 14 new labored examples within the LAMMPS, Quantum coffee, VASP and MedeA-VASP courses, together with computation of stress-strain habit of Si-CNT composite, mean-squared displacement (MSD) of ZrO2-Y2O3, band constitution and phonon spectra of silicon, and Mo-S battery procedure. It discusses tools as soon as thought of too dear yet which are now cost-efficient. New examples additionally contain a number of post-processed effects utilizing VESTA, VMD, VTST, and MedeA.

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Extra resources for Computational Materials Science: An Introduction, Second Edition

Sample text

However, one must be careful in the case of graphite since its layer-to-layer description is out of the range of the Tersoff potential. 4 Potentials for ionic solids Most potentials are short-ranged and approach to zero exponentially within the second neighbors. In ionic solids, however, ions can be polarized or charged, and the coulombic component can spread into a very long-range. 14) where Z1 and Z2 are charges of the constituting atoms. 15) where C1 and C2 are constants. The second term for coulomb interaction is the long-range interaction that decays as 1 rij .

Thus, the search path is in a zigzag fashion with many orthogonal steps that make this method relatively slow. 2 Conjugate gradients method This method starts by searching along the direction of the steepest descent, stops at a local minimum, and then figures out a new search direction based on both the new energy gradient (negative value of force between atoms) and the previous search direction. 13, the search path is rather straightforward with fewer steps that make this method relatively fast.

The method is conceptually very simple and easy to implement since it simply lets the atoms jump around randomly and picks the lowest energy state. It can equilibrate any degree of freedom, and no dynamics is needed since it is based on statistical mechanics. The so-called Metropolis MC normally simulates a thermodynamic ensemble and is used for energy minimization of the system. The Kinetic Monte Carlo (KMC) method generally works for activated processes such as atomic migration. The scale of the MC method is in microns and can thus treat microstructures of materials.

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Computational Materials Science: An Introduction, Second Edition by June Gunn Lee

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