Modern quantum chemistry with [open]molcas

Francesco Aquilante, Jochen Autschbach, Alberto Baiardi, Stefano Battaglia, Veniamin A. Borin, Liviu F. Chibotaru, Irene Conti, Luca De Vico, Mickaël Delcey, Ignacio Fdez Galván, Nicolas Ferré, Leon Freitag, Marco Garavelli, Xuejun Gong, Stefan Knecht, Ernst D. Larsson, Roland Lindh, Marcus Lundberg, Per Åke Malmqvist, Artur NenovJesper Norell, Michael Odelius, Massimo Olivucci, Thomas B. Pedersen, Laura Pedraza-González, Quan M. Phung, Kristine Pierloot, Markus Reiher, Igor Schapiro, Javier Segarra-Martí, Francesco Segatta, Luis Seijo, Saumik Sen, Dumitru Claudiu Sergentu, Christopher J. Stein, Liviu Ungur, Morgane Vacher, Alessio Valentini, Valera Veryazov*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

203 Scopus citations


MOLCAS/OpenMolcas is an ab initio electronic structure program providing a large set of computational methods from Hartree-Fock and density functional theory to various implementations of multiconfigurational theory. This article provides a comprehensive overview of the main features of the code, specifically reviewing the use of the code in previously reported chemical applications as well as more recent applications including the calculation of magnetic properties from optimized density matrix renormalization group wave functions.

Original languageAmerican English
Article number0004835
JournalJournal of Chemical Physics
Issue number21
StatePublished - 7 Jun 2020

Bibliographical note

Funding Information:
L.S. acknowledges financial support from Ministerio de Economía y Competitividad, Spain (Dirección General de Investigación y Gestión del Plan Nacional de I+D+i, Grant No. MAT2017-83553-P). J.S.-M. acknowledges support from the EU-H2020 Marie Curie Actions (AttoDNA, FP8-MSCA-IF, Grant No. 747662). I.S. gratefully acknowledges funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (Grant No. 678169 PhotoMutant). L.U. and X.G. gratefully acknowledge scientific Grant Nos. R-143-000-A80-114 and R-143-000-A65-133 from the National University of Singapore. Computational resources of the NSCC (ASPIRE-1, Grant No. 11001278) were used for this study.

Publisher Copyright:
© 2020 Author(s).


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