The potential energy landscape provides a conceptual and computational framework for investigating structure, dynamics, and thermodynamics in atomic and molecular science. This talk will present recent developments for global optimisation, quantum dynamics, thermodynamic properties of systems exhibiting broken ergodicity, and associated rare event dynamics, with applications to biophysics, self-organisation, and tunneling phenomena. Theory developed for molecular systems can be used to analyse the solution space forapplications to machine learning, self-consistent fields, and quantum computing.
Single funnel landscapes encode efficient self-organisation, explaining how the Levinthal Paradox can be overcome in proteins and in general. In contrast, multifunnel landscapes exhibit characteristic features in thermodynamic observables, such as the heat capacity, and multiple relaxation time scales in first passage time distributions. For example, a molecular switch corresponds to a double funnel landscape, while multifunnel landscapes underpin multifunctional capabilities of biomolecules. Understanding how to tune the landscape from self-organising to glassy properties represents a key design principle for systems with target emergent properties.
Selected Publications:
Nat. Commun (2024) 15, 8763. Design principles for energy transfer in the photosystem II supercomplex.
npj Quantum Information, 9, 75, 2023. Exact Electronic States With Shallow Quantum Circuits From Global Optimisation
Perspective: Journal of Physical Chemistry Letters (2022) 13, 6349. Dynamical Signatures of Multifunnel Energy Landscapes
Ann. Rev. Phys. Chem., 69, 401-425, (2017). Exploring Energy Landscapes
Chem. Commun, 53, 6974-6988 (2017). Exploring biomolecular energy landscapes
Perspective: Energy Landscapes for Machine Learning, PCCP, 19, 12585-12603, 2017.
Energy Landscapes: Some New Horizons, Curr. Op. Struct. Biol., 20, 3-10, 2010.
Energy Landscapes, Cambridge University Press, Cambridge, 2003
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