What type of study is this?

This is a Experimental Study study.

October 22, 2025

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

Key Points

dpdata enhances data management for atomistic machine learning potentials, achieving faster processing speeds.
Key utilities include automated train-test splitting and error-metric computation, facilitating efficient data handling.
Built on a flexible, plugin-based architecture, dpdata supports multiple file formats across scientific computing environments.
Memory savings and speed improvements are significant compared to traditional configuration-based tools like ASE.

Abstract

Seamless management of atomistic data sets is a critical prerequisite for the successful development and deployment of machine learning potentials (MLPs). Here, we present dpdata, an open-source Python library designed to streamline every aspect of MLP data handling. Built upon a flexible, plugin-based architecture, dpdata supports reading, writing, and converting between a broad range of file formats─from popular quantum-chemistry packages and molecular-dynamics engines to specialized MLP frameworks. Users may define custom data types, formats, drivers, and minimizers, enabling effortless extension to emerging software. Key utilities include automated train-test splitting, coordinate perturbation for active learning, outlier-energy removal, Δ-learning data set generation, error-metric computation, and unit conversion. Through efficient NumPy-backed storage and system-level operations, dpdata achieves significant memory saving and inference speedups over configuration-by-configuration tools such as ASE. We also highlight practical impact, with dpdata used across published studies, for format conversion, data storage, coordinate perturbation, and utilization in other projects for data processing.

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