dpdata: A Scalable Python Toolkit for Atomistic Machine Learning Data Sets
Bibliographic record
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.031 | 0.019 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".