Advanced search

Filter By:

Journal Check one or more journals to show results from those journals only.

Choose more journals

Article type Check one or more article types to show results from those article types only.
Subject Check one or more subjects to show results from those subjects only.
Date Choose a date option to show results from those dates only.

Custom date range

Clear all filters
Sort by:
Showing 1–2 of 2 results
Advanced filters: Author: Dongzhan Zhou Clear advanced filters

  • The authors introduce an evidential deep learning framework for machine learning interatomic potentials that efficiently provides robust uncertainty quantification, demonstrating its effectiveness across diverse atomic systems.

    • Han Xu
    • Taoyong Cui
    • Mao Su
    ResearchOpen Access
    Nature Communications
    Volume: 17, P: 1-11

  • Molecular dynamics simulations using machine learning interatomic potentials often face stability issues due to distribution shifts. Here, the authors develop an online test-time adaptation framework to improve generalization, allowing for more stable simulations without the need for additional training data.

    • Taoyong Cui
    • Chenyu Tang
    • Shufei Zhang
    ResearchOpen Access
    Nature Communications
    Volume: 16, P: 1-11