for questions about applications of machine learning algorithms to chemistry, not the machine learning methods themselves. Often bridging cheminformatics and computational chemistry, these methods consider how to represent chemical data to ML methods, accuracy thereof, and applications of chemical interest.
Machine Learning methods in chemistry have gained renewed interest both with improved accuracy and the availability of general machine learning methods and toolkits outside of chemistry.
Questions of relevance should focus on the chemical part of the machine learning, whether the representation of chemical concepts such as bonding or aromaticity to ML algorithms, or the application of chemical ML methods in computational-chemistry, cheminformatics, or related fields.
Important questions center on the representation of complex molecules and properties (often the subject of cheminformatics) and applications of methods to quantum-chemistry or computational-chemistry for predicting energies, thermochemistry, or other molecular properties.
