Grid: A Python library for molecular integration, interpolation, differentiation, and more.

Grid is a free and open-source Python library for constructing numerical grids to integrate, interpolate, and differentiate functions (e.g., molecular properties), with a strong emphasis on facilitating these operations in computational chemistry and conceptual density functional theory. Although designed, maintained, and released as a stand-alone Python library, Grid was originally developed for molecular integration, interpolation, and solving the Poisson equation in the HORTON and ChemTools packages. Grid is designed to be easy to use, extend, and maintain; this is why we use Python and adopt many principles of modern software development, including comprehensive documentation, extensive testing, continuous integration/delivery protocols, and package management. We leverage popular scientific packages, such as NumPy and SciPy, to ensure high efficiency and optimized performance in grid development. This article is the official release note of the Grid library showcasing its unique functionality and scope.

Identification of the preferential CO and SO2 adsorption sites within NOTT-401.

NOTT-401 was found to be a highly stable adsorbent for SO2 and CO with excellent cyclability and a straightforward regeneration at room temperature. Moreover, the preferential CO binding sites within the MOF material have been identified by experimental in situ DRIFT spectroscopy coupled with DFT and QTAIM calculations. Such preferential CO adsorption sites were correlated to identify the most significant SO2 interactions within NOTT-401. This study sheds light on the role of the thiophene and hydroxo functionality, for a MOF material, in the binding of SO2 or CO.

IOData: A python library for reading, writing, and converting computational chemistry file formats and generating input files.

IOData is a free and open-source Python library for parsing, storing, and converting various file formats commonly used by quantum chemistry, molecular dynamics, and plane-wave density-functional-theory software programs. In addition, IOData supports a flexible framework for generating input files for various software packages. While designed and released for stand-alone use, its original purpose was to facilitate the interoperability of various modules in the HORTON and ChemTools software packages with external (third-party) molecular quantum chemistry and solid-state density-functional-theory packages. IOData is designed to be easy to use, maintain, and extend; this is why we wrote IOData in Python and adopted many principles of modern software development, including comprehensive documentation, extensive testing, continuous integration/delivery protocols, and package management. This article is the official release note of the IOData library.

Implementation of the Molecular Electrostatic Potential over Graphics Processing Units.

The molecular electrostatic potential (MEP) generated by quantum chemistry methods and Gaussian functions is evaluated over graphics processing units (GPUs). This implementation is based on full-range Rys polynomials with nodes and weights obtained in each thread of a GPU. For high angular moments, the corresponding integral is solved using a one-dimension vertical recurrence relation. Thus, we computed the MEP with minimal approximations. We show that this implementation is stable and very efficient since the time consumed over GPUs is quite small compared with similar implementations over CPUs. The implementation was done by using CUDA-C programming techniques within the Graphics Processing Units for Atoms and Molecules (GPUAM) project, which has been designed to analyze quantum chemistry fields over heterogeneous computational resources. With this new scalar field GPUAM is a useful application for the quantum chemistry community, in particular for people interested in chemical reactivity analysis.

Analyzing ZnO clusters through the density-functional theory.

The potential energy surface of Zn n O n clusters (n = 2, 4, 6, 8) has been explored by using a simulated annealing method. For n = 2, 4, and 6, the CCSD(T)/TZP method was used as the reference, and from here it is shown that the M06-2X/TZP method gives the lowest deviations over PBE, PBE0, B3LYP, M06, and MP2 methods. Thus, with the M06-2X method we predict isomers of Zn n O n clusters, which coincide with some isomers reported previously. By using the atoms in molecules analysis, possible contacts between Zn and O atoms were found for all structures studied in this article. The bond paths involved in several clusters suggest that Zn n O n clusters can be obtained from the zincite (ZnO crystal), such an observation was confirmed for clusters with n = 2 - 9,18 and 20. The structure with n = 23 was obtained by the procedure presented here, from crystal information, which could be important to confirm experimental data delivered for n = 18 and 23.

Grid-based algorithm to search critical points, in the electron density, accelerated by graphics processing units.

Using a grid-based method to search the critical points in electron density, we show how to accelerate such a method with graphics processing units (GPUs). When the GPU implementation is contrasted with that used on central processing units (CPUs), we found a large difference between the time elapsed by both implementations: the smallest time is observed when GPUs are used. We tested two GPUs, one related with video games and other used for high-performance computing (HPC). By the side of the CPUs, two processors were tested, one used in common personal computers and other used for HPC, both of last generation. Although our parallel algorithm scales quite well on CPUs, the same implementation on GPUs runs around 10× faster than 16 CPUs, with any of the tested GPUs and CPUs. We have found what one GPU dedicated for video games can be used without any problem for our application, delivering a remarkable performance, in fact; this GPU competes against one HPC GPU, in particular when single-precision is used.