Apax: A Flexible and Performant Framework for the Development of Machine-Learned Interatomic Potentials
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Summary
This summary is machine-generated.Atomistic learned potentials in JAX (apax) is a new open-source software package for machine-learned interatomic potentials. It offers efficient training and inference, accelerating materials discovery through active learning and improved model accuracy.
Area Of Science
- Computational materials science
- Machine learning for scientific computing
Background
- Machine-learned interatomic potentials (MLIPs) are crucial for simulating materials at the atomic scale.
- Existing MLIP frameworks can be computationally intensive and lack flexibility for advanced applications like active learning.
Purpose Of The Study
- Introduce Atomistic learned potentials in JAX (apax), an open-source software package for MLIPs.
- Demonstrate apax's capabilities in accelerating materials simulations and active learning workflows.
Main Methods
- Developed apax leveraging the JAX framework for GPU acceleration and flexible model design.
- Implemented features for active learning: kernel-based data selection, uncertainty estimation, and enhanced sampling.
- Trained models on ionic liquid and Li3PO4 datasets, comparing performance against existing methods.
Main Results
- Continuously learning models in apax reduced training times by up to 85% for active learning.
- A Gaussian Moment Neural Network in apax achieved higher accuracy and 10x faster inference than Allegro.
- Demonstrated good scalability in data-parallel training and modularity for complex model ensembles.
Conclusions
- apax provides a flexible, efficient, and user-friendly platform for developing and deploying MLIPs.
- The software facilitates advanced applications like active learning, leading to faster and more accurate materials simulations.
- apax enables the development of novel MLIP models and accelerates the pace of materials discovery.
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