Area of Research
My research explores the behavior of molecular systems through computational methods. By integrating classical and quantum mechanical approaches, I aim to gain deeper insights into their structure, dynamics, and photophysical properties.
▶ Molecular Dynamics Simulation
Employing classical MD techniques, my research explores the time-dependent evolution of molecular systems, specifically macromolecules of biological interest. The work focuses on understanding dynamic structural shifts, analyzing solvation environments, and capturing critical conformational changes to uncover the fundamental physical principles driving molecular behavior.
▶ Enhanced Sampling Techniques
To overcome the timescale limitations inherent in classical simulations, I utilize enhanced sampling techniques. Methods such as Accelerated Molecular Dynamics (aMD), Gaussian Accelerated Molecular Dynamics (GaMD), Umbrella Sampling, and Replica Exchange Molecular Dynamics (REMD) are essential for mapping complex free energy landscapes. These approaches enable the efficient sampling of rare conformational events and the accurate determination of thermodynamic properties, providing a more complete picture of molecular flexibility and function.
▶ Electronic Structure Theory Calculations
A key part of my research focuses on modeling molecular systems at the quantum mechanical level. I use methods such as Density Functional Theory (DFT) and Time-Dependent DFT (TD-DFT), along with advanced wavefunction-based approaches, to study both ground and excited electronic states. This allows me to determine molecular structures, reaction energies, and photophysical properties, and to gain deeper insight into underlying reaction mechanisms.
▶ Multiscale QM/MM Modelling
To study chemical reactivity in complex systems, my work uses hybrid QM/MM approaches. In this method, the reactive region is treated using accurate quantum mechanics, while the surrounding environment is described with classical mechanics. This combined approach captures both electronic effects and environmental influences, enabling realistic simulations of processes such as enzymatic reactions and solvent effects that cannot be addressed by purely quantum or classical methods alone.
▶ Machine Learning In Chemistry
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