Exploring Energy Landscapes

• Published in: Annual review of physical chemistry Vol. 69; no. 1; pp. 401 - 425
• Main Author: Wales, David J
• Format: Journal Article
• Language: English
• Published: United States Annual Reviews 20.04.2018; Annual Reviews, Inc
• Subjects: Acceptance criteria; Artificial intelligence; Biomolecules; Coding; Condensed matter physics; Energy; energy landscapes; enhanced sampling; Global optimization; Machine learning; Mathematical analysis; Molecular chains; Morphology; Organic chemistry; Potential energy; rare events; Thermodynamic properties; Transportation networks; energy landscapes; machine learning; rare events; enhanced sampling; global optimization
• ISSN: 0066-426X; 1545-1593
• DOI: 10.1146/annurev-physchem-050317-021219

Recent advances in the potential energy landscapes approach are highlighted, including both theoretical and computational contributions. Treating the high dimensionality of molecular and condensed matter systems of contemporary interest is important for understanding how emergent properties are encoded in the landscape and for calculating these properties while faithfully representing barriers between different morphologies. The pathways characterized in full dimensionality, which are used to construct kinetic transition networks, may prove useful in guiding such calculations. The energy landscape perspective has also produced new procedures for structure prediction and analysis of thermodynamic properties. Basin-hopping global optimization, with alternative acceptance criteria and generalizations to multiple metric spaces, has been used to treat systems ranging from biomolecules to nanoalloy clusters and condensed matter. This review also illustrates how all this methodology, developed in the context of chemical physics, can be transferred to landscapes defined by cost functions associated with machine learning.