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Title: Learning free energy landscapes using artificial neural networks

Journal Article · · Journal of Chemical Physics
DOI: https://doi.org/10.1063/1.5018708 · OSTI ID:1425538
 [1]; ORCiD logo [1]
  1. Department of Chemical and Biomolecular Engineering, University of Notre Dame, Notre Dame, Indiana 46556, USA

Not Available

Sponsoring Organization:
USDOE
OSTI ID:
1425538
Journal Information:
Journal of Chemical Physics, Journal Name: Journal of Chemical Physics Journal Issue: 10 Vol. 148; ISSN 0021-9606
Publisher:
American Institute of PhysicsCopyright Statement
Country of Publication:
United States
Language:
English

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Measuring liquid crystal elastic constants with free energy perturbations journal January 2014
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Topology, structures, and energy landscapes of human chromosomes journal April 2015
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Graphite-diamond phase coexistence study employing a neural-network mapping of the ab initio potential energy surface journal March 2010
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Molecular Dynamics with On-the-Fly Machine Learning of Quantum-Mechanical Forces journal March 2015
Stochastic Neural Network Approach for Learning High-Dimensional Free Energy Surfaces journal October 2017
Generalized Neural-Network Representation of High-Dimensional Potential-Energy Surfaces journal April 2007
A computer-aided diagnostic system to characterize CT focal liver lesions: design and optimization of a neural network classifier journal September 2003
Enhancing Important Fluctuations: Rare Events and Metadynamics from a Conceptual Viewpoint journal May 2016
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A Practical Bayesian Framework for Backpropagation Networks journal May 1992

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