Energy landscapes: from molecules and nanodevices to glasses and machine learning

The potential energy landscape provides a conceptual and computational framework for
investigating structure, dynamics and thermodynamics in atomic and molecular science.
This talk will highlight connections between glassy systems and emergent phenomena in
clusters, biomolecules and soft matter. Applications will be presented to illustrate
new approaches for global optimisation, quantum dynamics, enhanced sampling of systems
exhibiting broken ergodicity, and rare event dynamics. The key aim is to explain how
the energy landscape perspective can unify our understanding of apparently disparate
systems. A range of applications will be presented, from spectroscopy, biomolecules,
and structural glass-formers, along with coarse-grained models and some recent results
for machine learning landscapes.

Effects of random pinning on the potential energy landscape of a supercooled liquid, JCP, 149, 114503, 2018.
Energy Landscapes for Machine Learning, PCCP, 19, 12585-12603, 2017.
Defining and quantifying frustration in the energy landscape, JCP, 146, 124103, 2017.
Exploring Biomolecular Energy Landscapes, Chem. Commun., 53, 6974, 2017
Energy landscapes for diffusion: Analysis of cage-breaking processes, JCP, 129, 164507, 2008.
Energy Landscapes, Cambridge University Press, Cambridge, 2003.