MOLDIS, our big data analytics platform for MOLecular DIScovery is now open for public access. Please click the logo below for more details.
We are a small research group working towards developing theoretical models to quantitatively forecast the properties of matter (molecules and extended materials) 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 of preference, are
- Chemical space design
- Quantum mechanics
- Machine learning and Big Data analytics
- Virtual high-throughput screening
- Femtosecond electron dynamics
- Anharmonic vibrational spectra
- Potential energy surfaces
- Computer-based science education
