The physical sciences community is increasingly taking advantage of the possibilities
offered by modern data science to solve problems in experimental chemistry and potentially …

In light of the pressing need for practical materials and molecular solutions to renewable
energy and health problems, to name just two examples, one wonders how to accelerate …

The field of computational chemistry has seen a significant increase in the integration of
machine learning concepts and algorithms. In this Perspective, we surveyed 179 open …

Across the past few decades, a gamut of technologies has emerged and been adopted as
part of enabling student learning. These technologies and digital tools have been explored …

Applications of machine learning in chemistry are many and varied, from prediction of
structure–property relationships, to modeling of potential energy surfaces for large scale …

Laboratory automation and data science are valuable new skills for all chemists, but most
pedagogical activities involving automation to date have focused on upper-level …

Infrared spectroscopy (IR) is a staple structural elucidation and characterization technique
because of its ability to identify functional groups and its ease of use. Interestingly, it allows …

Background Charge related heterogeneities of monoclonal antibody (mAb) based
therapeutic products are increasingly being considered as a critical quality attribute (CQA) …

The eChem project features an e-book published as a web page (10.30746/978-91-988114-
0-7), collecting a repository of Jupyter notebooks developed for the dual purpose of …

Acid–base chemistry, and in particular the Lewis acid–base model, is foundational to
understanding mechanistic ideas. This is due to the similarity in language chemists use to …