Collection 

Machine Learning Interatomic Potentials in Computational Materials

Submission status
Closed
Submission deadline

Machine learning interatomic potentials (MLIPs) have become an essential tool to enable long-time scale simulations of materials and molecules at unprecedented accuracies. The aim of this collection is to showcase cutting-edge developments in MLIP architectures, data generation techniques, and innovative sampling methods that push the boundaries of accuracy, efficiency, and applicability in atomic-scale simulations.

This Collection welcomes the following topics, including but not limited to:

  1. Architectural Advances
    1. New model architectures that enhance the accuracy, interpretability, or transferability of interatomic potentials.
    2. Novel algorithms for reducing model complexity while maintaining predictive power.
  2. Data Generation and Processing
    1. High-quality data generation strategies, including ab initio data curation and active learning techniques that minimize the need for computationally expensive data.
    2. Approaches for ensuring data diversity and coverage to improve MLIP robustness, especially for rare events.
    3. Advanced sampling methods that improve MLIP efficiency in handling diverse materials and molecular configurations.
  3. Applications
    1. In-depth studies demonstrating MLIPs in real-world applications, including material design, chemical reactions, and biological molecule simulations.
    2. Novel applications where MLIPs offer unique insights beyond traditional potential models or ab initio methods.
machine learning

Editors