AiiDA: automated interactive infrastructure and database for computational science

Abstract

Computational science has seen in the last decades a spectacular rise in the scope, breadth, and depth of its efforts. Notwithstanding this prevalence and impact, it is often still performed using the renaissance model of individual artisans gathered in a workshop, under the guidance of an established practitioner. Great benefits could follow instead from adopting concepts and tools coming from computer science to manage, preserve, and share these computational efforts. We illustrate here our paradigm sustaining such vision, based around the four pillars of Automation, Data, Environment, and Sharing. We then discuss its implementation in the open-source AiiDA platform (http://www.aiida.net), that has been tuned first to the demands of computational materials science. AiiDA's design is based on directed acyclic graphs to track the provenance of data and calculations, and ensure preservation and searchability. Remote computational resources are managed transparently, and automation is coupled with data storage to ensure reproducibility. Last, complex sequences of calculations can be encoded into scientific workflows. We believe that AiiDA's design and its sharing capabilities will encourage the creation of social ecosystems to disseminate codes, data, and scientific workflows.

Can artificial intelligence create the next wonder material?

“…Switzerland, for example, has created MAR-VEL, a network of institutes for computational materials science with the EPFL as its lead and Marzari as director. Using a new computational platform 13 , he is creating a database called Materials Cloud that he is using to search for 'two-dimensional' materials, such as graphene, that are made from just a single layer of atoms or molecules. Such materials could be used in applications ranging from nanoscale electronics to biomedical devices.…”

Band structure diagram paths based on crystallography

“…A collectively exhaustive predetermined list, applicable to any crystal, that contains labels and k -vector coefficients of relevant points in the BZ as well as a recommended band path connecting labeled points would be very useful. This is especially true for high-throughput calculations [1][2][3][4][5] where automatic band path generation is a necessity when the band structure is to be obtained. Making this comprehensive list is a non-trivial task.…”

“…Part of us recently outlined a computationally-friendly algorithm to transform basis vectors of the crystallographic conventional cell to the corresponding SC standard primitive cell [9], which therefore eases use of SC's definitions of distinctive BZ points and suggested band paths when starting from the crystallographic conventional cell. Secondly, the labels of distinctive BZ points differ in many cases between SC and the crystallographic convention used by 4 Cracknell et al (CDML) [10] and the Bilbao Crystallographic Server (BCS) [11][12][13][14][15].…”

Crystallography and Databases

“…Teaching (Gražulis et al 2015), powder identification (Lutterotti et al 2015), and source of data for computational material science (First & Floudas 2013, Pizzi et al 2016 are among the latest areas where the COD has proved useful as a source of data. Structures from the COD were used to produce 3D-printed models that find use in educational work , Scalfani et al 2016.…”