Energy landscapes: from molecules and nanodevices to machine learning

The potential energy landscape provides a conceptual and computational framework for investigating structure, dynamics and thermodynamics in atomic and molecular science. This lecture will summarise recent developments for global optimisation, enhanced sampling, and rare event dynamics. A variety of recent applications will be presented including proteins, nucleic acids, coarse-grained models, and design principles for self-assembly of mesoscopic structures, with recent results for global kinetics based on first passage time distributions.

Selected Publications:

• Design principles for energy transfer in the photosystem II supercomplex from kinetic transit ion networks, Nat. Commun (2024) 15, 8763
• Vibrational Energy Landscapes and Energy Flow in GPCRs, J. Phys. Chem. B (2024) 128, 7568
• Dynamical Signatures of Multifunnel Energy Landscapes, Journal of Physical Chemistry Letters (2022) 13, 6349
• Exploring Energy Landscapes, Ann. Rev. Phys. Chem., 69, 401-425, (2017)
• Exploring biomolecular energy landscapes Energy Landscapes: Some New Horizons, Chem. Commun, 53, 6974-6988 (2017)
• Energy Landscapes: Some New Horizons, Curr. Op. Struct. Biol., 20, 3-10, 2010
• Energy Landscapes, Cambridge University Press, Cambridge, 2003