Research

Simulation algorithms

Enhanced sampling simulations and potentials in excited states

Chiral hybrid perovskites as chiral emitters

Light switchable frameworks

Multiscale modeling of functional soft nanoassemblies

UV and ECD spectra predictions

Coordination of first row transition metals affecting structure and function

Simulation algorithms

We extensively use enhanced sampling simulations combined with force matching ab-initio derived potentials. Specifically we use well-tempered, (Barducci et al. Phys Rev. Lett. 2008, 100, 020603), parallel bias (Pfaendtner, Bonomi J. Chem Theory Comput. 2015, 11, 5062–5067) metadynamics (Laio, Parrinello Proc. Natl. Acad. Sci. USA 99, 12562-12566) within PLUMED (Bonomi et al. Nature Methods 2019, 16, 670–673).

From the free-energy minima ensembles, we predict a variety of properties (i.e. absorbance, emission, dichroism, reactivity) within ab-initio methods including DFT and TD-DFT framework. The property predictions can be oppurtunately coupled with learning-based algorithms.