Reliable deep learning in anomalous diffusion against out-of-distribution dynamics.

Area of Science:

• Molecular dynamics
• Biophysics
• Materials science

Background:

• Anomalous diffusion is key to understanding molecular behavior in biological and material systems.
• Deep learning excels at recognizing anomalous diffusion but fails with out-of-distribution (OOD) data.
• Limited training data distribution causes deep learning models to misinterpret unknown dynamics.

Purpose of the Study:

• To present a general framework for evaluating deep learning-based OOD dynamics detection methods.
• To develop a robust baseline approach for OOD dynamics detection and in-distribution recognition.

Main Methods:

• Developed a general framework for evaluating deep learning OOD detection.
• Created a baseline method for robust anomalous diffusion detection.
• Tested the method on diverse experimental systems.

Main Results:

• The baseline approach demonstrated robust OOD dynamics detection.
• The method accurately recognized in-distribution anomalous diffusion.
• Reliable characterization of complex behaviors in diverse systems was achieved.

Conclusions:

• The proposed framework and method enhance the reliability of deep learning for anomalous diffusion analysis.
• This approach enables accurate characterization of complex molecular dynamics across various scientific domains.
• It addresses the critical challenge of OOD scenarios in deep learning for diffusion studies.