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[Licence| Download | New Version Template] afak_v1_0.tar.gz(1379 Kbytes)
Manuscript Title: Amp: A modular approach to machine learning in atomistic simulations
Authors: Alireza Khorshidi, Andrew Peterson
Program title: Amp
Catalogue identifier: AFAK_v1_0
Distribution format: tar.gz
Journal reference: Comput. Phys. Commun. 207(2016)310
Programming language: Python, Fortran.
Computer: PC, Mac.
Operating system: Linux, Mac, Windows.
Has the code been vectorised or parallelized?: Yes
RAM: Variable, depending on the number and size of atomic systems.
Keywords: Potential energy surface, Neural networks, Atomic Simulation Environment (ASE), Density functional theory, Zernike polynominals.
Classification: 16.1, 2.1.

External routines: ASE, NumPy, SciPy, f2py, matplotlib

Nature of problem:
Atomic interactions within many-body systems typically have complicated functional forms, difficult to represent in simple pre-decided closed-forms.

Solution method:
Machine learning provides flexible functional forms that can be improved as new situations are encountered. Typically, interatomic potentials yield from machine learning simultaneously apply to different system sizes.

Unusual features:
Amp is as modular as possible, providing a framework for the user to create atomic environment descriptor and regression model at will. Moreover, it has Atomic Simulation Environment (ASE) interface, facilitating interactive collaboration with other electronic structure calculators within ASE.

Running time:
Variable, depending on the number and size of atomic systems.