Equivariant Evidential Deep Learning for Interatomic Potentials

📰 ArXiv cs.AI

Equivariant Evidential Deep Learning (EDL) is proposed for uncertainty quantification in machine learning interatomic potentials (MLIPs) in molecular dynamics simulations

advanced Published 6 Apr 2026
Action Steps
  1. Implement Equivariant Evidential Deep Learning (EDL) framework for MLIPs
  2. Quantify uncertainty in MLIPs using EDL
  3. Apply EDL to molecular dynamics simulations for improved reliability and accuracy
  4. Evaluate the performance of EDL in comparison to existing UQ approaches
Who Needs to Know This

Researchers and engineers working on molecular dynamics simulations and machine learning interatomic potentials can benefit from this approach to improve the reliability and accuracy of their models

Key Insight

💡 Equivariant Evidential Deep Learning provides a promising approach for uncertainty quantification in machine learning interatomic potentials

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💡 Equivariant Evidential Deep Learning for uncertainty quantification in MLIPs

Key Takeaways

Equivariant Evidential Deep Learning (EDL) is proposed for uncertainty quantification in machine learning interatomic potentials (MLIPs) in molecular dynamics simulations

Full Article

Title: Equivariant Evidential Deep Learning for Interatomic Potentials

Abstract:
arXiv:2602.10419v2 Announce Type: replace-cross Abstract: Uncertainty quantification (UQ) is critical for assessing the reliability of machine learning interatomic potentials (MLIPs) in molecular dynamics (MD) simulations, identifying extrapolation regimes and enabling uncertainty-aware workflows such as active learning for training dataset construction. Existing UQ approaches for MLIPs are often limited by high computational cost or suboptimal performance. Evidential deep learning (EDL) provide
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