Energy landscapes for a machine learning application to series data
- Resource Type
- Authors
- Jacob D. Stevenson; David J. Wales; Ritankar Das; Andrew J. Ballard
- Source
- Subject
- 010304 chemical physics
Artificial neural network
Series (mathematics)
Computer science
business.industry
General Physics and Astronomy
Function (mathematics)
Machine learning
computer.software_genre
01 natural sciences
Potential energy
Outcome (probability)
Maxima and minima
0103 physical sciences
Cluster (physics)
0801 Artificial Intelligence and Image Processing
Artificial intelligence
Physical and Theoretical Chemistry
010306 general physics
business
computer
Energy (signal processing)
- Language
- English
Methods developed to explore and characterise potential energy landscapes are applied to the corresponding landscapes obtained from optimisation of a cost function in machine learning. We consider neural network predictions for the outcome of local geometry optimisation in a triatomic cluster, where four distinct local minima exist. The accuracy of the predictions is compared for fits using data from single and multiple points in the series of atomic configurations resulting from local geometry optimisation and for alternative neural networks. The machine learning solution landscapes are visualised using disconnectivity graphs, and signatures in the effective heat capacity are analysed in terms of distributions of local minima and their properties.