<tt>metatensor</tt> and <tt>metatomic</tt> : Foundational libraries for interoperable atomistic machine learning
Abstract
Incorporation of machine learning (ML) techniques into atomic-scale modeling has proven to be an extremely effective strategy to improve the accuracy and reduce the computational cost of simulations. It also entails conceptual and practical challenges, as it involves combining very different mathematical foundations as well as software ecosystems that are very well developed in their own right but do not share many commonalities. To address these issues and facilitate the adoption of ML in atomistic simulations, we introduce two dedicated software libraries. The first one, metatensor, provides multi-platform and multi-language storage and manipulation of arrays with many potentially sparse indices, designed from the ground up for atomistic ML applications. By combining the actual values with metadata that describes their nature and that facilitates the handling of geometric information and gradients with respect to the atomic positions, metatensor provides a common framework to enable data sharing between ML software—typically written in Python—and established atomistic modeling tools—typically written in Fortran, C, or C++. The second library, metatomic, provides an interface to store an atomistic ML model and metadata about this model in a portable way, facilitating the implementation, training, and distribution of models, and their use across different simulation packages. We showcase a growing ecosystem of tools, including low-level libraries, training utilities, and interfaces with existing software packages, that demonstrate the effectiveness of metatensor and metatomic in bridging the gap between traditional simulation software and modern ML frameworks.
Article Details
Journal Info
The Journal of Chemical Physics
American Institute of Physics
Authors (15)
Filippo Bigi
Joseph W. Abbott
Laboratory of Computational Science and Modeling, Institute of Materials, École Polytechnique Fédérale de Lausanne , 1015 Lausanne,
Philip Loche
Laboratory of Computational Science and Modeling, Institute of Materials, École Polytechnique Fédérale de Lausanne , 1015 Lausanne,
Arslan Mazitov
Laboratory of Computational Science and Modeling, Institute of Materials, École Polytechnique Fédérale de Lausanne , 1015 Lausanne,
Davide Tisi
Laboratory of Computational Science and Modeling, Institute of Materials, École Polytechnique Fédérale de Lausanne , 1015 Lausanne,
Marcel F. Langer
Laboratory of Computational Science and Modeling, Institute of Materials, École Polytechnique Fédérale de Lausanne , 1015 Lausanne,
Alexander Goscinski
Laboratory of Computational Science and Modeling, Institute of Materials, École Polytechnique Fédérale de Lausanne , 1015 Lausanne,
Paolo Pegolo
Laboratory of Computational Science and Modeling, Institute of Materials, École Polytechnique Fédérale de Lausanne , 1015 Lausanne,
Sanggyu Chong
Laboratory of Computational Science and Modeling, Institute of Materials, École Polytechnique Fédérale de Lausanne 1 , 1015 Lausanne,
Rohit Goswami
Laboratory of Computational Science and Modeling, Institute of Materials, École Polytechnique Fédérale de Lausanne , 1015 Lausanne,
Pol Febrer
Laboratory of Computational Science and Modeling, Institute of Materials, École Polytechnique Fédérale de Lausanne , 1015 Lausanne,
Sofiia Chorna
Laboratory of Computational Science and Modeling, Institute of Materials, École Polytechnique Fédérale de Lausanne , 1015 Lausanne,
Matthias Kellner
Laboratory of Computational Science and Modeling, Institute of Materials, École Polytechnique Fédérale de Lausanne , 1015 Lausanne,
Michele Ceriotti
Laboratory of Computational Science and Modeling, Institute of Materials, École Polytechnique Fédérale de Lausanne 1 , 1015 Lausanne,
Guillaume Fraux
Laboratory of Computational Science and Modeling, Institute of Materials, École Polytechnique Fédérale de Lausanne , 1015 Lausanne,