Data Science Meets Chemistry

Cover art by Tara Zepel and Jason Hein.

Scientists have long benefitted from and contributed to the development of quantitative methods to reveal patterns in structure-property relationships across all branches of chemistry ranging from materials to synthetic organic to biological. Recent advances in computing power, software and algorithms, as well as increases in data availability from experiment or computation, have led to dramatic progress in the complexity of statistical techniques applied to chemistry. This special issue will cover how this new wave of research, in combination with more established techniques, has already changed the manner in which chemists are addressing and understanding the physical world around them. This issue will include contributions that demonstrate the profound impact data science techniques have had in chemistry including chemical and materials synthesis, catalyst and materials design, and overhauling the models used in traditional theoretical or computational chemistry. As such techniques begin to demonstrate key advances in the chemical sciences, machine learning and statistical techniques are becoming a core part of the chemist's toolkit. This Special Issue, guest-edited by Heather J. Kulik (MIT) and Matt Sigman (University of Utah), will discuss the advances that are starting to reshape the field as data science meets chemistry.