OpenMX Interface for the Atomic Simulation Environment

OpenMX Interface for the Atomic Simulation Environment

썸네일 Challenge 애게서 업로드 하였습니다. 19. 7. 4 오전 11:49
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The Atomic Simulation Environment (ASE) package written in Python is aimed at providing an easy, flexible and customizable environment for density-functional-theory (DFT) calculations[1]. Currently, the ASE package supports interfaces over 30 Calculators including ABINIT, SIESTA, Quantum Espresso, and VASP. Currently, the ASE package supports interfaces over 30 Calculators including ABINIT, SIESTA, Quantum Espresso and VASP. While most of ab initio calculations require massive computational resources and opt for high efficiency, the Python language behind the ASE package provides a copious number of library modules which make swift code developments possible. Here, we like to take advantage of both the OpenMX code and the ASE/Python modules by implementing the OpenMX interface for the ASE. For example, combining the LAMMPS and OpenMX codes, we can seamlessly perform molecular dynamics simulations without compensating coding efficiencies. Even benchmarking the OpenMX results against VASP is a breeze. Since the modules in ASE are highly scalable, we can utilize some of the features of ASE modules, e.g., the phonon module, for the OpenMX calculations. We hope that this platform can be used for combining DFT calculations with machine learning and big data, where the famous Python modules like Spark and TensorFlow can be employed to conduct deep learning research.
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19. 7. 4 오후 1:46
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The Atomic Simulation Environment (ASE) package written in Python is aimed at providing an easy, flexible and customizable environment for density-functional-theory (DFT) calculations[1]. Currently, the ASE package supports interfaces over 30 Calculators including ABINIT, SIESTA, Quantum Espresso, and VASP. Currently, the ASE package supports interfaces over 30 Calculators including ABINIT, SIESTA, Quantum Espresso and VASP. While most of ab initio calculations require massive computational resources and opt for high efficiency, the Python language behind the ASE package provides a copious number of library modules which make swift code developments possible. Here, we like to take advantage of both the OpenMX code and the ASE/Python modules by implementing the OpenMX interface for the ASE. For example, combining the LAMMPS and OpenMX codes, we can seamlessly perform molecular dynamics simulations without compensating coding efficiencies. Even benchmarking the OpenMX results against VASP is a breeze. Since the modules in ASE are highly scalable, we can utilize some of the features of ASE modules, e.g., the phonon module, for the OpenMX calculations. We hope that this platform can be used for combining DFT calculations with machine learning and big data, where the famous Python modules like Spark and TensorFlow can be employed to conduct deep learning research.
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