Computational Chemistry Capabilities

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 Chemical Structures
  • Absolute and Relative Energies
  • Electronic Properties (Charge Distribution, Molecular Orbitals, etc.)
  • Spectroscopic Properties (e.g. IR, NMR)
  • Bond Dissociation Energies (BDE)
  • 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 thus produced help guide reaction development. Using these predictions, J-Star Research scientists can gain deeper mechanistic insight, rationalize experimental observations, and identify improved strategies and routes towards their targeted molecule.

Using up to 40 cores of parallel processing power, the computational software at J-Star Research provides a wide range of capabilities. A few of our most frequently employed methods include:

Density Functional Theory (DFT)

Molecular Mechanics (MM)

Semi-empirical quantum chemistry methods

Møller-Plesset perturbation theory (MP2, MP3)

ONIOM (QM/MM methods)