Predicting Molecular Properties via Computational Chemistry

Computational Chemistry is a growing field intersecting with nearly all disciplines of chemistry, as its ability to accurately model and predict chemical reactivity, molecular structures and experimental properties is crucial to modern molecular design and synthesis. Using relatively routine methods, we can obtain accurate predictions of various important chemical properties, strongly complementing classical experimental results.

Properties that can be predicted from computational models include:

  • 3-D molecular geometries, torsions potential energy scan, conformers population distribution in solutions
  • Stereoisomers population distribution in solvent systems
  • Absolute and Relative Free Energies of chemical reactions in solvent systems
  • Electronic Properties (charge distribution, molecular orbitals, reactivity indices, etc.)
  • Spectroscopic Properties (IR, NMR, UV-vis, ECD, VCD, Raman)
  • Bond Dissociation Energies (BDE)
  • Free energy of hydrogen/proton abstraction scan in a solvent system
  • Acid Dissociation Constants (pKa)

In addition to molecular properties, entire or partial reaction pathways can be examined at the molecular scale, giving insights into reaction kinetics, transition states, reaction orders, product distributions and more.

The reasonable predictions produced through computational investigation can help guide reaction development by understanding non-trivial molecular interactions. Using these predictions, J-Star Research scientists can gain deeper mechanistic insight, rationalize experimental observations, and identify improved strategies and routes towards their targeted molecules.

Using up to 24-cores/48-threads and 192GB of RAM, the computational resources at J-Star Research provides a wide range of capabilities. A few of our most frequently employed software packages include:

Gaussian 16



And a few of the most commonly used methods include:

Dispersion-corrected Density Functional Theory (DFT-D)

Molecular Mechanics (MM)

Semi-empirical quantum chemistry methods (GFNn-xTB)

ONIOM (QM/MM methods)