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Portably parallel construction of a configuration‐interaction wave function from a matrix–product state using the Charm ++ framework
Author(s) -
Wang Ting,
Ma Yingjin,
Zhao Lian,
Jiang Jinrong
Publication year - 2020
Publication title -
journal of computational chemistry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.907
H-Index - 188
eISSN - 1096-987X
pISSN - 0192-8651
DOI - 10.1002/jcc.26424
Subject(s) - population , quantum entanglement , asynchronous communication , wave function , hilbert space , computer science , chemistry , computational chemistry , parallel computing , physics , quantum mechanics , quantum , computer network , demography , sociology
The construction of configuration‐interaction (CI) expansions from a matrix product state (MPS) involves numerous matrix operations and the skillful sampling of important configurations in a large Hilbert space. In this work, we present an efficient procedure for constructing CI expansions from MPS employing the parallel object‐oriented Charm++ programming framework, upon which automatic load‐balancing and object migrating facilities can be employed. This procedure was employed in the MPS‐to‐CI utility (Moritz et al., J. Chem. Phys. 2007, 126 , 224109), the sampling‐reconstructed complete active‐space algorithm (SR‐CAS, Boguslawski et al., J. Chem. Phys. 2011, 134 , 224101), and the entanglement‐driven genetic algorithm (EDGA, Luo et al., J. Chem. Theory Comput. 2017, 13 , 4699). It enhances productivity and allows the sampling programs to evolve to their population‐expansion versions, for example, EDGA with population expansion (PE‐EDGA). Further, examples of 1,2‐dioxetanone and firefly dioxetanone anion (FDO − ) molecules demonstrated the following: (a) parallel efficiencies can be persistently improved by simply by increasing the proportions of the asynchronous executions and (b) a sampled CAS‐type CI wave function of a bi‐radical‐state FDO − molecule utilizing the full valence (30e,26o) active space can be constructed within a few hours with using thousands of cores.