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Updated: Dec 8, 2025

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

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Committee neural network potentials control generalization errors and enable active learning.

Christoph Schran1, Krystof Brezina1, Ondrej Marsalek1

  • 1Charles University, Faculty of Mathematics and Physics, Ke Karlovu 3, 121 16 Prague 2, Czech Republic.

The Journal of Chemical Physics
|September 16, 2020
PubMed
Summary
This summary is machine-generated.

Committee models enhance machine learning accuracy by averaging multiple neural network potentials. This approach minimizes calculations and controls generalization error for robust models, as demonstrated for water simulations.

Related Experiment Videos

Last Updated: Dec 8, 2025

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
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Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

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Area of Science:

  • Computational chemistry
  • Materials science
  • Machine learning

Background:

  • Committee models in machine learning improve accuracy and generalization.
  • Adapting committee models to interatomic potentials based on artificial neural networks is novel.
  • Existing methods may require extensive ab initio calculations.

Purpose of the Study:

  • To adapt committee models for artificial neural network-based interatomic potentials.
  • To utilize committee disagreement for active learning and generalization error control.
  • To develop robust machine learning potentials with minimal ab initio calculations.

Main Methods:

  • Developed committee neural network potentials sharing atomic environment descriptors.
  • Employed committee disagreement for active learning to expand training sets.
  • Monitored and biased committee disagreement to control generalization error during simulations.
  • Applied the methodology to create a committee model for condensed-phase water, including nuclear quantum effects.

Main Results:

  • The committee average outperformed individual models.
  • Committee disagreement effectively identified relevant configurations for training.
  • The developed model accurately described water across various conditions (liquid, ice phases, air-water interface) with nuclear quantum effects.
  • Achieved excellent results with only 814 reference ab initio calculations.

Conclusions:

  • Committee models offer a robust approach for developing accurate interatomic potentials.
  • Active learning guided by committee disagreement minimizes computational cost.
  • This methodology facilitates the systematic development of machine learning models for diverse systems, incorporating nuclear quantum effects.