Related papers: GGCHEMPY: A pure Python-based gas-grain chemical c…
The PySCF package has emerged as a powerful and flexible open-source platform for quantum chemistry simulations. However, the efficiency of electronic structure calculations can vary significantly depending on the choice of computational…
We introduce the cosmological HYPER code based on an innovative hydro-particle-mesh (HPM) algorithm for efficient and rapid simulations of gas and dark matter. For the HPM algorithm, we update the approach of Gnedin & Hui (1998) to expand…
The emission lines from ionized nebulae allow us to determine their physical and chemical properties, along with the interstellar extinction. "pyEQUIB" is a pure Python open-source package including several application programming interface…
Star formation in molecular clouds is intimately linked to their internal mass distribution. We present an unprecedentedly detailed analysis of the column density structure of a high-mass, filamentary molecular cloud, namely IRDC…
We examine several different simplified approaches for modelling the chemistry of CO in three-dimensional numerical simulations of turbulent molecular clouds. We compare the different models both by looking at the behaviour of integrated…
We present an extended version of the 2-phase gas-grain code NAUTILUS to the 3-phase modelling of gas and grain chemistry of cold cores. In this model, both the mantle and the surface are considered as chemically active. We also take into…
We have built an open-source software system for the modeling of biomolecular reaction networks, SloppyCell, which is written in Python and makes substantial use of third-party libraries for numerics, visualization, and parallel…
We describe how Python can be leveraged to streamline the curation, modelling and dissemination of drug discovery data as well as the development of innovative, freely available tools for the related scientific community. We look at various…
Automatic design of organic materials requires black-box optimization in a vast chemical space. In conventional molecular design algorithms, a molecule is built as a combination of predetermined fragments. Recently, deep neural network…
High-level scripting languages are in many ways polar opposites to GPUs. GPUs are highly parallel, subject to hardware subtleties, and designed for maximum throughput, and they offer a tremendous advance in the performance achievable for a…
Bayesian Networks (BNs) are used in various fields for modeling, prediction, and decision making. pgmpy is a python package that provides a collection of algorithms and tools to work with BNs and related models. It implements algorithms for…
We have designed an improved algorithm that enables us to simulate the chemistry of cold dense interstellar clouds with a full gas-grain reaction network. The chemistry is treated by a unified microscopic-macroscopic Monte Carlo approach…
As modern scientific simulations grow ever more in size and complexity, even their analysis and post-processing becomes increasingly demanding, calling for the use of HPC resources and methods. yt is a parallel, open source post-processing…
The absorption and emission of light by exoplanet atmospheres encode details of atmospheric composition, temperature, and dynamics. Fundamentally, simulating these processes requires detailed knowledge of the opacity of gases within an…
Machine learning solutions are very popular in the field of chemoinformatics, where they have numerous applications, such as novel drug discovery or molecular property prediction. Molecular fingerprints are algorithms commonly used for…
In this work, we examine the performance, energy efficiency and usability when using Python for developing HPC codes running on the GPU. We investigate the portability of performance and energy efficiency between CUDA and OpenCL; between…
Magpy is a C++ accelerated Python package for modelling and simulating the magnetic dynamics of nano-sized particles. Nanoparticles are modelled as a system of three-dimensional macrospins and simulated with a set of coupled stochastic…
The past decade has led to significant improvements in our understanding of the physical structure of the molecular cores of cold dark clouds. Observational efforts, in combination with improved knowledge of cloud structure, now provide…
Our main purpose is to estimate the effect of assuming uniform density on the line-of-sight in PDR chemistry models, compared to a more realistic distribution for which total gas densities may well vary by several orders of magnitude. A…
In this work, we present scikit-fingerprints, a Python package for computation of molecular fingerprints for applications in chemoinformatics. Our library offers an industry-standard scikit-learn interface, allowing intuitive usage and easy…