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

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