TURBOMOLE: Modular program suite for ab initio quantum-chemical and condensed-matter simulations

Abstract

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.

Neural-network assisted study of nitrogen atom dynamics on amorphous solid water – I. adsorption and desorption

“…The codes we have employed for the generation of the training set are, respectively: a) GFN2-xTB calculations with the xTB code (Bannwarth et al 2019;Grimme et al 2017) and DL-Poly/Chemshell as MD driver (Sherwood et al 2003;Todorov et al 2006;Metz et al 2014) b) PM7 calculations with Gaussian16 (Stewart 2013;Frisch et al 2016) and DL-Poly/Chemshell as MD Driver, c) DFT energy and gradient calculations with Turbomole 7.4.1 (Grimme et al 2015;Balasubramani et al 2020).…”

“…Training of all models was performed within the Tensorflow framework (Abadi et al 2015) for 5000 epochs on an NVIDIA GeForce GTX-1080-Ti-11GB GPU each. The training of the GM-NN model required at most four days.…”

Stabilizing a metalloid {Zn12} unit within a polymetallide environment in [K2Zn20Bi16]6−

“…Quantum chemical investigation of the bonding in [K 2 Zn 20 Bi 16 ] 6−(1a). We optimized33,34 the geometric structure at DFT level (TPSS/dhf-TZVP/grid m3). The calculated molecular…”

Synthesis and Characterization of Push‐Pull Aza‐BODIPY Dyes Towards Application in NIR‐II Photothermal Therapy

“…To account for the solvent effects, the conductor-like screening model (COSMO) [6] with the dielectric constant of CHCl 3 (ɛ = 4.81) was applied in all of the DFT calculations, which were performed using TURBOMOLE software package. [7] Figure 4 shows that the LUMO energy levels obtained from the CV are in good agreement with the DFT method; the LUMO energy differences are in the range of 0.030-0.142 eV and most of the HOMO energy levels obtained from the DFT method match well with the CV results with the largest difference of 0.212 eV for CNÀ Br. Therefore, the B3LYP/6-311G results confirm the HOMO and LUMO energy levels measured in the electrochemical experiments.…”