.png){kind=icon}
Return
dpdata: A Scalable Python Toolkit for Atomistic Machine Learning Data Sets
.png){kind=icon}
PRE
AI
DOI：10.1021/acs.jcim.5c01767.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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, Delta-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.
Keywords:
MOLECULAR-DYNAMICS
Journal
IF:
5.3
Papers:
9.1K
Citations:
4.0W
Organization
View more organizations
Cited Papers
ShareSaveE(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials.png)
NATURE COMMUNICATIONS
OAAI
NATURE COMMUNICATIONSIF15.7
OAAIShareSaveShareSave
Fast plane wave density functional theory molecular dynamics calculations on multi-GPU machines
PREAI
ShareSaveShareSaveShareSaveShareSave View more