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OpenMolcas: From Source Code to Insight
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Number of Authors: 632019 (English)In: Journal of Chemical Theory and Computation, ISSN 1549-9618, E-ISSN 1549-9626, Vol. 15, no 11, p. 5925-5964Article in journal (Refereed) Published
Abstract [en]

In this Article we describe the OpenMolcas environment and invite the computational chemistry community to collaborate. The open-source project already includes a large number of new developments realized during the transition from the commercial MOLCAS product to the open-source platform. The paper initially describes the technical details of the new software development platform. This is followed by brief presentations of many new methods, implementations, and features of the OpenMolcas program suite. These developments include novel wave function methods such as stochastic complete active space self-consistent field, density matrix renormalization group (DMRG) methods, and hybrid multiconfigurational wave function and density functional theory models. Some of these implementations include an array of additional options and functionalities. The paper proceeds and describes developments related to explorations of potential energy surfaces. Here we present methods for the optimization of conical intersections, the simulation of adiabatic and nonadiabatic molecular dynamics, and interfaces to tools for semiclassical and quantum mechanical nuclear dynamics. Furthermore, the Article describes features unique to simulations of spectroscopic and magnetic phenomena such as the exact semiclassical description of the interaction between light and matter, various X-ray processes, magnetic circular dichroism, and properties. Finally, the paper describes a number of built-in and add-on features to support the OpenMolcas platform with postcalculation analysis and visualization, a multiscale simulation option using frozen-density embedding theory, and new electronic and muonic basis sets.

Place, publisher, year, edition, pages
2019. Vol. 15, no 11, p. 5925-5964
National Category
Chemical Sciences Physical Sciences
Identifiers
URN: urn:nbn:se:su:diva-176577DOI: 10.1021/acs.jctc.9b00532ISI: 000497260300014PubMedID: 31509407OAI: oai:DiVA.org:su-176577DiVA, id: diva2:1377391
Available from: 2019-12-11 Created: 2019-12-11 Last updated: 2019-12-11Bibliographically approved

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Bao, Jie J.Bokarev, Sergey I.Carlson, Rebecca K.Dong, Sijia S.Freitag, LeonNorell, JesperQuan, Manh
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