Division/Committee: [COMP] Division of Computers in Chemistry

The developments in computer architectures and software have enabled large-scale modeling of chemical systems in recent years. Handling big data is becoming commonplace in academia as well as industry, as novel tools such as machine learning have made it necessary to handle millions of data points at a time. Simultaneously to the big data revolution, free and open source software has made breakthroughs in computational chemistry. Not only are the main software packages of machine learning free and open source, but many traditional libraries and programs domain-specific to computational chemistry have also followed suit. The aim of this symposium is to cast light on the developments catalyzed by the existing rich ecosystem of free and open source projects, including but not limited to applications on big data. We are especially interested in submissions on innovative work made possible by taking advantage of the broad ecosystem of free and open source projects, relying not only on a single project but rather on the integration of many different projects. Submissions on other aspects of FOSS in chemistry, including novel algorithms, methods of data analysis, as well as software packages are also welcome.

Y. Jane Tseng

National Taiwan University

Appearances