1 - Computational chemistry models to help provide insight into a range of atomic and molecular systems via the prediction of their general properties, including:
- Thermodynamic properties (heat of formation, enthalpy, entropy, equilibrium constant)
- Kinetic properties (reaction rate, transition-state structure, atomic mobility, diffusion coefficient)
- Ground- and excited-state geometries and general properties (e.g. dipole moment, molecular orbitals, electron densities)
- Packing modes, interactions and structures
- Absorption, emission and vibrational spectra
- NMR chemical shifts
2 - Deep Learning / Machine Learning models to predict properties and optimise processes.
3 - Deep Learning / Machine Learning training via Hands-On courses delivered personally or online.
Please contact us for further information!