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metatensor and metatomic: Foundational libraries for interoperable atomistic machine learning.

Filippo Bigi1, Joseph W Abbott1, Philip Loche1

  • 1Laboratory of Computational Science and Modeling, Institute of Materials, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland.

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New software libraries, metatensor and metatomic, bridge machine learning (ML) and atomistic simulations. These tools facilitate data sharing and model portability, enhancing ML adoption in materials science simulations.

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Area of Science:

  • Computational Materials Science
  • Machine Learning in Physics
  • Scientific Software Development

Background:

  • Machine learning (ML) significantly enhances atomic-scale simulations by improving accuracy and reducing computational costs.
  • Integrating ML with traditional atomistic modeling faces challenges due to disparate mathematical foundations and software ecosystems.
  • A need exists for tools that facilitate seamless data exchange and model compatibility between ML frameworks and established simulation packages.

Purpose of the Study:

  • To introduce novel software libraries, metatensor and metatomic, designed to overcome integration challenges between ML and atomistic simulations.
  • To provide a common framework for data handling and model storage, promoting wider adoption of ML in materials modeling.
  • To enable efficient sharing and utilization of ML models across diverse simulation software.

Main Methods:

  • Development of `metatensor`: a multi-platform, multi-language library for storing and manipulating arrays with metadata, specifically for atomistic ML.
  • Implementation of `metatomic`: a library providing a portable interface for storing atomistic ML models and associated metadata.
  • Demonstration of an integrated ecosystem of tools, including libraries, training utilities, and interfaces with existing simulation packages.

Main Results:

  • `metatensor` enables unified data representation and manipulation, facilitating interoperability between Python-based ML software and Fortran/C/C++-based simulation tools.
  • `metatomic` ensures portable storage and distribution of ML models, simplifying their implementation and use across different simulation environments.
  • The developed ecosystem showcases the practical effectiveness of `metatensor` and `metatomic` in bridging the gap between traditional and modern computational approaches.

Conclusions:

  • `metatensor` and `metatomic` provide essential infrastructure for advancing ML applications in atomistic simulations.
  • These libraries effectively address the challenges of combining different software ecosystems, fostering collaboration and innovation.
  • The demonstrated ecosystem accelerates the integration of ML into mainstream materials modeling workflows.