As the tools of computational quantum chemistry have continued to mature, larger and more complex molecular systems have become amenable to computational study. However, studies of these complex systems often require the execution of enormous numbers of computations, which can be a tedious and error‐prone process if done manually. We have developed a suite of free, open‐source tools to facilitate the automation of quantum chemistry workflows. These tools are collected under the organization QChASM (Quantum Chemistry Automation and Structure Manipulation) and include functionality for building and manipulating complex molecular structures and performing routine tasks (AaronTools), a toolkit for automating TS optimizations and predictions of the outcomes of selective homogeneous catalytic reactions, and a plug‐in for UCSF ChimeraX that provides a graphical interface for building complex molecular structures and representing output from quantum chemistry computations. These tools are described below, with a focus on the recent Python implementation of AaronTools.
This article is categorized under: Structure and Mechanism > Reaction Mechanisms and Catalysis Software > Quantum Chemistry
The Molecular Sciences Software Institute's (MolSSI) Quantum Chemistry Archive (QCA
This article is categorized under: Electronic Structure Theory > Ab Initio Electronic Structure Methods Software > Quantum Chemistry Data Science > Computer Algorithms and Programming
Protein‐only RNase P (PRORP) is an essential enzyme responsible for the 5′ maturation of precursor tRNAs (pre‐tRNAs). PRORPs are classified into three categories with unique molecular architectures, although all three classes of PRORPs share a mechanism and have similar active sites. Single subunit PRORPs, like those found in plants, have multiple isoforms with different localizations, substrate specificities, and temperature sensitivities. Most recently,
This article is categorized under: RNA Structure and Dynamics > RNA Structure, Dynamics and Chemistry RNA Structure and Dynamics > Influence of RNA Structure in Biological Systems
In silico materials design is hampered by the computational complexity of Kohn–Sham DFT, which scales cubically with the system size. Owing to the development of new‐generation kinetic energy density functionals (KEDFs), orbital‐free DFT (OFDFT) can now be successfully applied to a large class of semiconductors and such finite systems as quantum dots and metal clusters. In this work, we present DFTpy, an open‐source software implementing OFDFT written entirely in Python 3 and outsourcing the computationally expensive operations to third‐party modules, such as NumPy and SciPy. When fast simulations are in order, DFTpy exploits the fast Fourier transforms from PyFFTW. New‐generation, nonlocal and density‐dependent‐kernel KEDFs are made computationally efficient by employing linear splines and other methods for fast kernel builds. We showcase DFTpy by solving for the electronic structure of a million‐atom system of aluminum metal which was computed on a single CPU. The Python 3 implementation is object‐oriented, opening the door to easy implementation of new features. As an example, we present a time‐dependent OFDFT implementation (hydrodynamic DFT) which we use to compute the spectra of small metal clusters recovering qualitatively the time‐dependent Kohn–Sham DFT result. The Python codebase allows for easy implementation of application programming interfaces. We showcase the combination of DFTpy and ASE for molecular dynamics simulations of liquid metals. DFTpy is released under the MIT license.
This article is categorized under: Software > Quantum Chemistry Electronic Structure Theory > Density Functional Theory Data Science > Computer Algorithms and Programming
Modern vibrational spectroscopy is more than just an analytical tool. Information about the electronic structure of a molecule, the strength of its bonds, and its conformational flexibility is encoded in the normal vibrational modes. On the other hand, normal vibrational modes are generally delocalized, which hinders the direct access to this information, attainable only via local vibration modes and associated local properties. Konkoli and Cremer provided an ingenious solution to this problem by deriving local vibrational modes from the fundamental normal modes, obtained in the harmonic approximation of the potential, via mass‐decoupled Euler–Lagrange equations. This review gives a general introduction into the local vibrational mode theory of Konkoli and Cremer, elucidating how this theory unifies earlier attempts to obtain easy to interpret chemical information from vibrational spectroscopy: (a) the local mode theory furnishes bond strength descriptors derived from force constant matrices with a physical basis, (b) provides the highly sought after extension of the Badger rule to polyatomic molecules, (c) and offers a simpler way to derive localized vibrations compared to the complex route via overtone spectroscopy. Successful applications are presented, including a new measure of bond strength, a new detailed analysis of infrared/Raman spectra, and the recent extension to periodic systems, opening a new avenue for the characterization of bonding in crystals. At the end of this review the LMODEA software is introduced, which performs the local mode analysis (with minimal computational costs) after a harmonic vibrational frequency calculation optionally using measured frequencies as additional input.
This article is categorized under: Structure and Mechanism > Molecular Structures Theoretical and Physical Chemistry > Spectroscopy Software > Quantum Chemistry Electronic Structure Theory > Ab Initio Electronic Structure Methods
We describe a bundle for UCSF ChimeraX called SEQCROW that provides advanced structure editing capabilities and quantum chemistry utilities designed for complex organic and organometallic compounds. SEQCROW includes graphical presets and bond editing tools that facilitate the generation of publication‐quality molecular structure figures while also allowing users to build molecular structures quickly and efficiently by mapping new ligands onto existing organometallic complexes as well as adding rings and substituents. Other capabilities include the ability to visualize vibrational modes and simulated IR spectra, to compute and visualize molecular descriptors including percent buried volume, ligand cone angles, and Sterimol parameters, to process thermochemical corrections from quantum mechanical computations, to generate input files for ORCA, Psi4, and Gaussian, and to run and manage computational jobs.
