ASE

The atomic simulation environment - a Python library for working with atoms. The atomic simulation environment (ASE) is a software package written in the Python programming language with the aim of setting up, steering, and analyzing atomistic simulations. In ASE, tasks are fully scripted in Python. The powerful syntax of Python combined with the NumPy array library make it possible to perform very complex simulation tasks. For example, a sequence of calculations may be performed with the use of a simple ’for-loop’ construction. Calculations of energy, forces, stresses and other quantities are performed through interfaces to many external electronic structure codes or force fields using a uniform interface. On top of this calculator interface, ASE provides modules for performing many standard simulation tasks such as structure optimization, molecular dynamics, handling of constraints and performing nudged elastic band calculations


References in zbMATH (referenced in 13 articles )

Showing results 1 to 13 of 13.
Sorted by year (citations)

  1. Katarina Brlec; Daniel W. Davies; David O. Scanlon: Surfaxe: Systematic surface calculations (2021) not zbMATH
  2. Morten Gjerding, Thorbjørn Skovhus, Asbjørn Rasmussen, Fabian Bertoldo, Ask Hjorth Larsen, Jens Jørgen Mortensen, Kristian Sommer Thygesen: Atomic Simulation Recipes - a Python framework and library for automated workflows (2021) arXiv
  3. T. L. Underwood, J. A. Purton, J. R. H. Manning, A. V. Brukhno, K. Stratford, T. Düren, N. B. Wilding, S. C. Parker: dlmontepython: A Python library for automation and analysis of Monte Carlo molecular simulations (2021) arXiv
  4. He Ma, Wennie Wang, Siyoung Kim, Man-Hin Cheng, Marco Govoni, Giulia Galli: PyCDFT: A Python package for constrained density functional theory (2020) arXiv
  5. J. Magnus Rahm; Paul Erhart: WulffPack: A Python package for Wulff constructions (2020) not zbMATH
  6. Matthew L. Evans; Andrew J. Morris: matador: a Python library for analysing, curating and performing high-throughput density-functional theory calculations (2020) not zbMATH
  7. M. V. Klymenko, J. A. Vaitkus, J. S. Smith, J. H. Cole: NanoNET: an extendable Python framework for semi-empirical tight-binding models (2020) arXiv
  8. Stansbury, C.; Lanzara, A.: PyARPES: An analysis framework for multimodal angle-resolved photoemission spectroscopies (2020) not zbMATH
  9. Brian C. Ferrari: AutoGAMESS: A Python package for automation of GAMESS(US) Raman calculations (2019) not zbMATH
  10. Flores-Vergara, A.; García-Guerrero, E. E.; Inzunza-González, E.; López-Bonilla, O. R.; Rodríguez-Orozco, Eduardo; Cárdenas-Valdez, Jose R.; Tlelo-Cuautle, E.: Implementing a chaotic cryptosystem in a 64-bit embedded system by using multiple-precision arithmetic (2019)
  11. Scott Fredericks, Dean Sayre, Qiang Zhu: PyXtal: a Python Library for Crystal Structure Generation and Symmetry Analysis (2019) arXiv
  12. Yunqi Shao, Matti Hellström, Pavlin D. Mitev, Lisanne Knijff, Chao Zhang: PiNN: A Python Library for Building Atomic Neural Networks of Molecules and Materials (2019) arXiv
  13. Alex M Ganose; Adam J Jackson; David O Scanlon: sumo: Command-line tools for plotting and analysis of periodic ab initio calculations (2018) not zbMATH