Machine learning in the Molecular Sciences
The goal of this research is to develop new chemical descriptors that can be used as inputs to machine learning (ML) algorithms to describe chemical and physical properties of molecules and materials. This will involve quantum chemistry and statistical mechanics calculations, training of ML models, and writing and communicating results. Students will interact with the professor and graduate students to perform the calculations and write the codes for the ML development, testing, and validation.
Our group and research lies at the unique intersection of machine learning and the chemical and molecular sciences. The student will have regular interactions with the graduate student lead and scheduled meetings with the professor. The student will also attend our weekly group meetings where they will be exposed to all of the ongoing research efforts in the group. This project in particular has great potential for technological innovation as we seek to develop models capable of describing and predicting properties of molecules and materials. We are looking to apply these models for societally relevant applications like refrigerant design, drug discovery, energy storage, separation media, etc.