TURBOMOLE: Modular program suite for ab initio quantum-chemical and condensed-matter simulations
By Sree Ganesh Balasubramani, Guo P. Chen, S. Coriani, M. Diedenhofen, Marius S Frank, Yannick J. Franzke, Filipp Furche, R. Grotjahn, M. Harding, Christof Hättig and 25 more. Journal of Chemical Physics, 2020
860 citations recorded by the source corpus.
TURBOMOLE is a collaborative, multi-national software development project aiming to provide highly efficient and stable computational tools for quantum chemical simulations of molecules, clusters, periodic systems, and solutions. The TURBOMOLE software suite is optimized for widely available, inexpensive, and resource-efficient hardware such as multi-core workstations and small computer clusters. TURBOMOLE specializes in electronic structure methods with outstanding accuracy–cost ratio, such as density functional theory including local hybrids and the random phase approximation (RPA), GW-Bethe–Salpeter methods, second-order Møller–Plesset theory, and explicitly correlated coupled-cluster methods. TURBOMOLE is based on Gaussian basis sets and has been pivotal for the development of many fast and low-scaling algorithms in the past three decades, such as integral-direct methods, fast multipole methods, the resolution-of-the-identity approximation, imaginary frequency integration, Laplace transform, and pair natural orbital methods. This review focuses on recent additions to TURBOMOLE’s functionality, including excited-state methods, RPA and Green’s function methods, relativistic approaches, high-order molecular properties, solvation effects, and periodic systems. A variety of illustrative applications along with accuracy and timing data are discussed. Moreover, available interfaces to users as well as other software are summarized. TURBOMOLE’s current licensing, distribution, and support model are discussed, and an overview of TURBOMOLE’s development workflow is provided. Challenges such as communication and outreach, software infrastructure, and funding are highlighted.
Subfields: Atomic and Molecular Physics, and Optics, Catalysis
View Paper: https://www.semanticscholar.org/paper/00d5213b0bddff30ab69a5b77a9bcf47e82f21bc
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In Atomic and Molecular Physics, and Optics
Citations in the corpus
- Accurate and numerically efficient r$^2$SCAN meta-generalized gradient approximation (2020, methodology): “The grids were chosen from Tur-bomole [29, 30] grid levels 1-7. numerical challenges while changing as little as possible from the parent functional [22].”