NECI: N-Electron Configuration Interaction with an emphasis on state-of-the-art stochastic methods

We present NECI, a state-of-the-art implementation of the Full Configuration Interaction Quantum Monte Carlo (FCIQMC) algorithm, a method based on a stochastic application of the Hamiltonian matrix on a sparse sampling of the wave function. The program utilizes a very powerful parallelization and sc...

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Published inThe Journal of chemical physics Vol. 153; no. 3; pp. 034107 - 34131
Main Authors Guther, Kai, Anderson, Robert J., Blunt, Nick S., Bogdanov, Nikolay A., Cleland, Deidre, Dattani, Nike, Dobrautz, Werner, Ghanem, Khaldoon, Jeszenszki, Peter, Liebermann, Niklas, Manni, Giovanni Li, Lozovoi, Alexander Y., Luo, Hongjun, Ma, Dongxia, Merz, Florian, Overy, Catherine, Rampp, Markus, Samanta, Pradipta Kumar, Schwarz, Lauretta R., Shepherd, James J., Smart, Simon D., Vitale, Eugenio, Weser, Oskar, Booth, George H., Alavi, Ali
Format Journal Article
LanguageEnglish
Published Melville American Institute of Physics 21.07.2020
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Abstract We present NECI, a state-of-the-art implementation of the Full Configuration Interaction Quantum Monte Carlo (FCIQMC) algorithm, a method based on a stochastic application of the Hamiltonian matrix on a sparse sampling of the wave function. The program utilizes a very powerful parallelization and scales efficiently to more than 24 000 central processing unit cores. In this paper, we describe the core functionalities of NECI and its recent developments. This includes the capabilities to calculate ground and excited state energies, properties via the one- and two-body reduced density matrices, as well as spectral and Green’s functions for ab initio and model systems. A number of enhancements of the bare FCIQMC algorithm are available within NECI, allowing us to use a partially deterministic formulation of the algorithm, working in a spin-adapted basis or supporting transcorrelated Hamiltonians. NECI supports the FCIDUMP file format for integrals, supplying a convenient interface to numerous quantum chemistry programs, and it is licensed under GPL-3.0.
AbstractList We present NECI, a state-of-the-art implementation of the Full Configuration Interaction Quantum Monte Carlo (FCIQMC) algorithm, a method based on a stochastic application of the Hamiltonian matrix on a sparse sampling of the wave function. The program utilizes a very powerful parallelization and scales efficiently to more than 24 000 central processing unit cores. In this paper, we describe the core functionalities of NECI and its recent developments. This includes the capabilities to calculate ground and excited state energies, properties via the one- and two-body reduced density matrices, as well as spectral and Green’s functions for ab initio and model systems. A number of enhancements of the bare FCIQMC algorithm are available within NECI, allowing us to use a partially deterministic formulation of the algorithm, working in a spin-adapted basis or supporting transcorrelated Hamiltonians. NECI supports the FCIDUMP file format for integrals, supplying a convenient interface to numerous quantum chemistry programs, and it is licensed under GPL-3.0.
We present NECI, a state-of-the-art implementation of the Full Configuration Interaction Quantum Monte Carlo (FCIQMC) algorithm, a method based on a stochastic application of the Hamiltonian matrix on a sparse sampling of the wave function. The program utilizes a very powerful parallelization and scales efficiently to more than 24 000 central processing unit cores. In this paper, we describe the core functionalities of NECI and its recent developments. This includes the capabilities to calculate ground and excited state energies, properties via the one- and two-body reduced density matrices, as well as spectral and Green's functions for ab initio and model systems. A number of enhancements of the bare FCIQMC algorithm are available within NECI, allowing us to use a partially deterministic formulation of the algorithm, working in a spin-adapted basis or supporting transcorrelated Hamiltonians. NECI supports the FCIDUMP file format for integrals, supplying a convenient interface to numerous quantum chemistry programs, and it is licensed under GPL-3.0.We present NECI, a state-of-the-art implementation of the Full Configuration Interaction Quantum Monte Carlo (FCIQMC) algorithm, a method based on a stochastic application of the Hamiltonian matrix on a sparse sampling of the wave function. The program utilizes a very powerful parallelization and scales efficiently to more than 24 000 central processing unit cores. In this paper, we describe the core functionalities of NECI and its recent developments. This includes the capabilities to calculate ground and excited state energies, properties via the one- and two-body reduced density matrices, as well as spectral and Green's functions for ab initio and model systems. A number of enhancements of the bare FCIQMC algorithm are available within NECI, allowing us to use a partially deterministic formulation of the algorithm, working in a spin-adapted basis or supporting transcorrelated Hamiltonians. NECI supports the FCIDUMP file format for integrals, supplying a convenient interface to numerous quantum chemistry programs, and it is licensed under GPL-3.0.
Author Blunt, Nick S.
Bogdanov, Nikolay A.
Vitale, Eugenio
Dobrautz, Werner
Cleland, Deidre
Ma, Dongxia
Anderson, Robert J.
Manni, Giovanni Li
Rampp, Markus
Alavi, Ali
Guther, Kai
Ghanem, Khaldoon
Luo, Hongjun
Lozovoi, Alexander Y.
Dattani, Nike
Merz, Florian
Schwarz, Lauretta R.
Booth, George H.
Weser, Oskar
Liebermann, Niklas
Jeszenszki, Peter
Samanta, Pradipta Kumar
Smart, Simon D.
Overy, Catherine
Shepherd, James J.
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Snippet We present NECI, a state-of-the-art implementation of the Full Configuration Interaction Quantum Monte Carlo (FCIQMC) algorithm, a method based on a stochastic...
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SubjectTerms Algorithms
Central processing units
Computer simulation
Configuration interaction
CPUs
Hamiltonian functions
Parallel processing
Physics
Quantum chemistry
Wave functions
Title NECI: N-Electron Configuration Interaction with an emphasis on state-of-the-art stochastic methods
URI http://dx.doi.org/10.1063/5.0005754
https://www.proquest.com/docview/2424222660
https://www.proquest.com/docview/2427522028
Volume 153
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