Our motivation is to quantitatively forecast the properties of matter and simulate related fundamental processes, to a degree of accuracy that is relevant to complement outcomes from state-of-the-art experiments. To this end, we deploy a wide range of deductive quantum mechanical approximations along with inductive approaches based on supervised/unsupervised machine learning, under the broad theme Deductive and Inductive Modeling of Matter. Topics of interest to us, in no particular order, are
- Chemical space design
- Many body physics
- Machine learning and Big Data analytics
- Virtual screening
- Femtosecond phenomena
- Anharmonic vibrational spectra
- Potential energy surfaces
- Computer-based science education