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The threshold algorithm: Description of the methodology and new developments.

Sridhar NeelamrajuChristina OligschlegerJ Christian Schön
Published in: The Journal of chemical physics (2018)
Understanding the dynamics of complex systems requires the investigation of their energy landscape. In particular, the flow of probability on such landscapes is a central feature in visualizing the time evolution of complex systems. To obtain such flows, and the concomitant stable states of the systems and the generalized barriers among them, the threshold algorithm has been developed. Here, we describe the methodology of this approach starting from the fundamental concepts in complex energy landscapes and present recent new developments, the threshold-minimization algorithm and the molecular dynamics threshold algorithm. For applications of these new algorithms, we draw on landscape studies of three disaccharide molecules: lactose, maltose, and sucrose.
Keyphrases
  • machine learning
  • deep learning
  • molecular dynamics
  • neural network
  • density functional theory
  • single cell