This article was accepted into the corpus but its outbound wikilinks were never NER-processed — typical at the deepest BFS hop or when the run's entity cap was reached. No expansion funnel to show.
| ASE (Atomic Simulation Environment) | |
|---|---|
| Name | ASE (Atomic Simulation Environment) |
| Released | 2004 |
| Programming language | Python |
| Operating system | Cross-platform |
| Genre | Scientific software |
| License | BSD-style |
ASE (Atomic Simulation Environment) is a Python-based open-source framework for setting up, manipulating, running, and analyzing atomistic simulations. It connects high-level workflow control with a wide range of electronic structure and interatomic potential packages, enabling researchers at CERN, Lawrence Berkeley National Laboratory, Max Planck Society, Argonne National Laboratory, and Los Alamos National Laboratory to integrate tools like VASP, Quantum ESPRESSO, GPAW, LAMMPS, and CP2K into reproducible pipelines. ASE is widely used alongside projects such as NumPy, SciPy, Matplotlib, pandas, and Jupyter Notebook in computational materials science and computational chemistry environments like Oak Ridge National Laboratory and Harvard University research groups.
ASE provides a modular set of Python modules and command-line utilities that abstract common tasks in atomistic modeling, allowing interoperability between simulation codes such as VASP, Quantum ESPRESSO, GPAW, CP2K, and SIESTA. Developers and users in institutions like MIT, Stanford University, ETH Zurich, Imperial College London, and University of Cambridge leverage ASE to combine calculators from NWChem, ORCA, Gaussian, CASTEP, and FHI-aims with analysis tools from Materials Project, AFLOW, and OQMD. The project bridges computational ecosystems involving Python Software Foundation ecosystems and high-performance computing centers including NERSC and PRACE facilities.
ASE originated in the early 2000s, emerging in activities at research centers such as University of Twente and groups collaborating with Forschungszentrum Jülich and Daresbury Laboratory. Development has been influenced by collaborations with researchers from Royal Institute of Technology, University of Helsinki, and Chalmers University of Technology, and has integrated ideas from codes like CPMD and DMol3. Over time ASE attracted contributors affiliated with University of Vienna, Karlsruhe Institute of Technology, University of California, Berkeley, Yale University, and University College London, integrating features inspired by projects at Sandia National Laboratories and Industrial Technology Research Institute.
ASE's architecture centers on Python objects representing atoms, calculators, optimizers, and trajectories. Core features include geometry construction and manipulation used in studies at Los Alamos National Laboratory, Brookhaven National Laboratory, and Lawrence Livermore National Laboratory; support for force and energy evaluations interoperable with VASP, Quantum ESPRESSO, and GPAW; and optimization algorithms paralleling methods from Cambridge Crystallographic Data Centre. ASE provides hooks for molecular dynamics employed in workflows at Columbia University and University of Illinois Urbana-Champaign, and analysis routines compatible with visualization packages such as VMD, OVITO, and PyMOL. The modular design echoes software engineering practices from GNU Project and OpenStack with unit testing traditions from Travis CI and GitHub-based collaboration patterns endorsed by Linus Torvalds.
ASE exposes interfaces to a broad set of electronic structure and force-field codes including VASP, Quantum ESPRESSO, GPAW, CP2K, SIESTA, CASTEP, FHI-aims, NWChem, ORCA, Gaussian, DL_POLY, LAMMPS, DL_FIELD, ReaxFF, TURBOMOLE, ABINIT, BigDFT, and Siesta. Community contributions have added adapters for specialized packages used at University of Chicago, Princeton University, University of Tokyo, and Tohoku University enabling integration with databases like Materials Project, NOMAD Laboratory, and Citrine Informatics. ASE also interops with workflow managers such as FireWorks, AiiDA, Signac, Snakemake, and Dask for high-throughput studies.
Typical ASE workflows begin with structure generation using primitives and converters aligned with standards from Crystallography Open Database, followed by calculator assignment and relaxations using optimizers like BFGS, FIRE, and conjugate gradient algorithms adopted in research at ETH Zurich and KTH Royal Institute of Technology. Trajectory I/O supports formats used by CIF archives and visualization in VESTA and ParaView. ASE scripts are commonly executed on compute infrastructures such as XSEDE, PRACE, HLRS, and cloud platforms used by Google Cloud Platform and Amazon Web Services research initiatives. Integration with analysis stacks from scikit-learn, scikit-optimize, TensorFlow, and PyTorch enables machine-learning potentials akin to projects at DeepMind, Google Research, Facebook AI Research, and industrial labs like BASF and Dow.
ASE emphasizes lightweight Python orchestration while delegating heavy numerical work to backend calculators designed for parallel execution on systems maintained by NERSC, Oak Ridge Leadership Computing Facility, and European Centre for Medium-Range Weather Forecasts. Scalability studies often pair ASE with MPI-parallel codes like VASP and Quantum ESPRESSO on supercomputers such as Summit, Fugaku, and Frontier. Performance tuning in workflows leverages job scheduling systems like SLURM, HTCondor, and PBS Professional used at academic centers including Cornell University and Princeton Plasma Physics Laboratory.
ASE is used in research on catalysis at Max Planck Institute for Chemical Energy Conversion, battery materials at Toyota Research Institute and Argonne National Laboratory, two-dimensional materials at Columbia University and University of Manchester, and surface science groups at Oak Ridge National Laboratory and Sandia National Laboratories. Case studies demonstrate ASE-driven workflows for high-throughput screening in projects like Materials Project, AFLOWLIB, and OQMD, and in machine-learning interatomic potential development linked to initiatives at ETH Zurich and Flatiron Institute.
ASE is distributed under a permissive BSD-style license and has an active developer and user community collaborating via platforms such as GitHub, mailing lists tied to institutions like ICSMR, and workshops hosted at conferences including MRS Fall Meeting, APS March Meeting, Gordon Research Conferences, and ECOSS. The ecosystem includes tutorials from NanoHub, training sessions at EMBO-linked schools, and contributions from researchers at University of California, Santa Barbara, University of Texas at Austin, Tsinghua University, and Peking University.
Category:Computational chemistry software Category:Materials science software