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NumPy est une bibliothèque pour langage de programmation Python, destinée à manipuler des matrices ou tableaux multidimensionnels ainsi que des fonctions mathématiques opérant sur ces tableaux. Plus précisément, cette bibliothèque logicielle libre et open source fournit de multiples fonctions permettant notamment de créer directement un tableau depuis un fichier ou au contraire de sauvegarder un tableau dans un fichier, et manipuler des vecteurs, matrices et polynômes. NumPy est la base de SciPy, regroupement de bibliothèques Python autour du calcul scientifique. Historique Le langage de programmation Python n'a pas été conçu à l'origine pour le calcul numérique. Cependant, il a très tôt attiré l'attention de la communauté scientifique et technique. En 1995, le groupe d'intérêt spécial (SIG) matrix-sig a été fondé dans le but de définir un paquetage de calcul matriciel. Parmi ses membres, Guido van Rossum, concepteur et développeur de Python, a étendu sa syntaxe, et en

i-PI 2.0: A universal force engine for advanced molecular simulations

Sébastien Pierre Bienvenue, Michele Ceriotti, Bingqing Cheng, Alice Cuzzocrea, Benjamin Aaron Helfrecht, Przemyslaw Juda, Venkat Kapil, Robert Horst Meissner, Riccardo Petraglia, Mariana Rossi Carvalho, Gareth Aneurin Tribello, David Mark Wilkins

Progress in the atomic-scale modeling of matter over the past decade has been tremendous. This progress has been brought about by improvements in methods for evaluating interatomic forces that work by either solving the electronic structure problem explicitly, or by computing accurate approximations of the solution and by the development of techniques that use the Born-Oppenheimer (BO) forces to move the atoms on the BO potential energy surface. As a consequence of these developments it is now possible to identify stable or metastable states, to sample configurations consistent with the appropriate thermodynamic ensemble, and to estimate the kinetics of reactions and phase transitions. All too often, however, progress is slowed down by the bottleneck associated with implementing new optimization algorithms and/or sampling techniques into the many existing electronic-structure and empirical-potential codes. To address this problem, we are thus releasing a new version of the i-PI software. This piece of software is an easily extensible framework for implementing advanced atomistic simulation techniques using interatomic potentials and forces calculated by an external driver code. While the original version of the code (Ceriotti et al., 2014) was developed with a focus on path integral molecular dynamics techniques, this second release of i-PI not only includes several new advanced path integral methods, but also offers other classes of algorithms. In other words, i-PI is moving towards becoming a universal force engine that is both modular and tightly coupled to the driver codes that evaluate the potential energy surface and its derivatives. Program summary Program Title: i-PI Program Files doi: http://dx.doLorg/10.17632/x792grbm9g.1 Licensing provisions: GPLv3, MIT Programming language: Python External routines/libraries: NumPy Nature of problem: Lowering the implementation barrier to bring state-of-the-art sampling and atomistic modeling techniques to ab initio and empirical potentials programs. Solution method: Advanced sampling methods, including path-integral molecular dynamics techniques, are implemented in a Python interface. Any electronic structure code can be patched to receive the atomic coordinates from the Python interface, and to return the forces and energy that are used to integrate the equations of motion, optimize atomic geometries, etc. Restrictions: This code does not compute interatomic potentials, although the distribution includes sample driver codes that can be used to test different techniques using a few simple model force fields. (C) 2018 Elsevier B.V. All rights reserved.

ELSEVIER SCIENCE BV2019

i-PI 2.0: A universal force engine for advanced molecular simulations

ELSEVIER SCIENCE BV2019