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dpdata: A Scalable Python Toolkit for Atomistic Machine Learning Data Sets.

Jinzhe Zeng1,2,3, Xingliang Peng4, Yong-Bin Zhuang5

  • 1School of Artificial Intelligence and Data Science, Unversity of Science and Technology of China, Hefei 230026, P. R. China.

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|October 20, 2025
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Summary
This summary is machine-generated.

Managing atomistic data for machine learning potentials (MLPs) is streamlined by dpdata, an open-source Python library. It simplifies data handling, conversion, and processing, improving efficiency for MLP development.

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Area of Science:

  • Computational Chemistry
  • Materials Science
  • Machine Learning

Background:

  • Effective management of atomistic datasets is crucial for developing and deploying machine learning potentials (MLPs).
  • Existing tools can be inefficient for handling diverse data formats and processing needs in MLP workflows.

Purpose of the Study:

  • To introduce dpdata, an open-source Python library designed to simplify and optimize the handling of atomistic data for MLPs.
  • To provide a flexible and extensible solution for reading, writing, converting, and processing diverse datasets.

Main Methods:

  • Developed a plugin-based architecture supporting various file formats from quantum chemistry and molecular dynamics software.
  • Implemented key utilities for data preprocessing, including train-test splitting, coordinate perturbation, outlier removal, and unit conversion.
  • Utilized efficient NumPy-backed storage and system-level operations for memory and speed optimization.

Main Results:

  • dpdata supports a wide array of file formats and allows for custom extensions to new software.
  • Achieved significant memory savings and inference speedups compared to configuration-by-configuration tools like ASE.
  • Demonstrated practical impact through its use in published studies for data conversion, storage, and processing.

Conclusions:

  • dpdata offers a robust and efficient solution for managing atomistic data in machine learning potential development.
  • The library's flexibility and performance enhancements facilitate streamlined workflows and accelerate research in computational materials science.