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    Extending and Modifying LAMMPS Writing Your Own Source Code. A pragmatic guide to extending LAMMPS as per custom simulation requirements Dr. Shafat Mubin, Jichen Li, Dr. Steven Plimpton

    (ebook) (audiobook) (audiobook) Książka w języku angielskim
    Extending and Modifying LAMMPS Writing Your Own Source Code. A pragmatic guide to extending LAMMPS as per custom simulation requirements Dr. Shafat Mubin, Jichen Li, Dr. Steven Plimpton - okladka książki

    Extending and Modifying LAMMPS Writing Your Own Source Code. A pragmatic guide to extending LAMMPS as per custom simulation requirements Dr. Shafat Mubin, Jichen Li, Dr. Steven Plimpton - okladka książki

    Extending and Modifying LAMMPS Writing Your Own Source Code. A pragmatic guide to extending LAMMPS as per custom simulation requirements Dr. Shafat Mubin, Jichen Li, Dr. Steven Plimpton - audiobook MP3

    Extending and Modifying LAMMPS Writing Your Own Source Code. A pragmatic guide to extending LAMMPS as per custom simulation requirements Dr. Shafat Mubin, Jichen Li, Dr. Steven Plimpton - audiobook CD

    Autorzy:
    Dr. Shafat Mubin, Jichen Li, Dr. Steven Plimpton
    Wydawnictwo:
    Packt Publishing (Z chęcią przeczytam książkę w języku polskim)
    Ocena:
    Bądź pierwszym, który oceni tę książkę
    Stron:
    394
    Dostępne formaty:
         PDF
         ePub
         Mobi
    LAMMPS is one of the most widely used tools for running simulations for research in molecular dynamics. While the tool itself is fairly easy to use, more often than not you’ll need to customize it to meet your specific simulation requirements. Extending and Modifying LAMMPS bridges this learning gap and helps you achieve this by writing custom code to add new features to LAMMPS source code. Written by ardent supporters of LAMMPS, this practical guide will enable you to extend the capabilities of LAMMPS with the help of step-by-step explanations of essential concepts, practical examples, and self-assessment questions.
    This LAMMPS book provides a hands-on approach to implementing associated methodologies that will get you up and running and productive in no time. You’ll begin with a short introduction to the internal mechanisms of LAMMPS, and gradually transition to an overview of the source code along with a tutorial on modifying it. As you advance, you’ll understand the structure, syntax, and organization of LAMMPS source code, and be able to write your own source code extensions to LAMMPS that implement features beyond the ones available in standard downloadable versions.
    By the end of this book, you’ll have learned how to add your own extensions and modifications to the LAMMPS source code that can implement features that suit your simulation requirements.

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    O autorach książki

    Dr. Shafat Mubin (PhD, Physics, Penn State) is an assistant professor of physics at Valdosta State University. Since his graduate student days, he has worked with molecular simulations using primarily LAMMPS and has investigated a variety of simulation systems employing a wide array of techniques. He possesses extensive experience in writing custom routines and extending the LAMMPS source code, and hosts his own website to instruct and demonstrate the same to other users. At present, he is engaged in computational physics research including molecular simulations, and endeavours to train undergraduate students in computational techniques to help them better prepare for careers in physics.
    Jichen Li (graduated from Qingdao University of Science and Technology) is now studying for his master's degree at the University of Science and Technology of China. He used LAMMPS to conduct many molecular simulations to explore the relationship between polymer microstructure and macro mechanical and rheological properties. He developed several modeling and post-processing frameworks for LAMMPS and had a certain understanding of its program architecture. He dedicated to the community construction and wrote many columns and tutorials for LAMMPS starters. At present, he is working on the trans-scale simulation and the combination of deep learning and simulation.

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