Abstract
Driven by the unprecedented computational power available to scientific research, the use of computers in solid-state physics, chemistry and materials science has been on a continuous rise. This review focuses on the software used for the simulation of matter at the atomic scale. We provide a comprehensive overview of major codes in the field, and analyze how citations to these codes in the academic literature have evolved since 2010. An interactive version of the underlying data set is available at https://atomistic.software .
Highlights
Scientists today have unprecedented access to computational power
This indicates that the use of permissive licenses is a recent phenomenon in the space of atomistic simulation engines, and may follow in the footsteps of the open-source community at large which is exhibiting a similar trend: according to an analysis of over 4 million open-source packages by WhiteSource [41], the use of permissive open-source licenses has nearly doubled from 41% in 2012 to 76% in 2020, with the Apache and MIT licenses alone accounting for more than half of all licenses that year
Citations of atomistic simulation engines in the collection have grown at an annual compound growth rate of ∼8%, roughly twice the 4% growth rate seen in the publication of peer-reviewed articles in science and engineering over the last decade [42]
Summary
Scientists today have unprecedented access to computational power. This statement would be unremarkable, were it not for the extent to which computational power has exploded. Many of these atomistic simulation engines have been around during this explosion of computational power, continuously evolving to take advantage of new algorithms, processor architectures, increasing parallelism and, more recently, dedicated accelerator hardware Over time, they have developed from instruments for specialists to proven and tested tools in the arsenal of practitioners in physics, chemistry, and materials science. While statistics like these are by no means accurate measures of code complexity (and developers follow different approaches to packaging and outsourcing of functionality to external libraries), they suggest that many of these code bases are too large to be sustained by any single person. For those wanting to know more, the following sections provide details on the methodologies used, and discuss some of the trends that can be observed
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