Amp: A modular approach to machine learning in atomistic simulations

Computer Physics Communications

Volumn 207, Issue , 2016, Pages 310-324

  Khorshidi, Alireza ^a   Peterson, Andrew A ^a  

^a Brown University   (United States)

Author keywords

Atomic Simulation Environment (ASE); Density functional theory; Neural networks; Potential energy surface; Zernike polynomials

Indexed keywords

ARTIFICIAL INTELLIGENCE; ATOMS; CALCULATIONS; COMPUTATION THEORY; COMPUTATIONAL CHEMISTRY; COMPUTER OPERATING SYSTEMS; COMPUTER SOFTWARE; DENSITY FUNCTIONAL THEORY; ELECTRONIC STRUCTURE; FORTRAN (PROGRAMMING LANGUAGE); HIGH LEVEL LANGUAGES; MOLECULAR DYNAMICS; MOLECULAR PHYSICS; NEURAL NETWORKS; OPEN SOURCE SOFTWARE; OPEN SYSTEMS; POTENTIAL ENERGY; POTENTIAL ENERGY SURFACES; QUANTUM CHEMISTRY; REGRESSION ANALYSIS; SOFTWARE TESTING; TIME MEASUREMENT; WINDOWS OPERATING SYSTEM;

ATOMIC SIMULATIONS; ELECTRONIC STRUCTURE CALCULATIONS; INTERATOMIC POTENTIAL; MACHINE LEARNING TECHNIQUES; MOLECULAR DYNAMICS SIMULATIONS; PHENOMENA AND PROPERTIES; PYTHON SCRIPTING LANGUAGES; ZERNIKE POLYNOMIALS;

LEARNING SYSTEMS;

EID: 84991660111     PISSN: 00104655     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.cpc.2016.05.010     Document Type: Article

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