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New Atompack format accelerates atomistic ML dataset training

By PulseAugur Editorial · [1 sources] ·

Researchers have developed Atompack, a new storage and distribution layer specifically designed for atomistic machine learning training datasets. This format is optimized for the common workload of repeatedly reading shuffled molecular records during training, offering significant performance improvements over existing solutions like HDF5 and LMDB. Atompack achieves up to 96x faster shuffled reads and produces artifacts that are 79% smaller, making it more efficient for both training and public distribution of large scientific datasets. AI

IMPACT Optimizes data handling for atomistic ML, potentially speeding up research and development in fields like materials science and drug discovery.

RANK_REASON Research paper detailing a new data storage format for ML. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New Atompack format accelerates atomistic ML dataset training

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Research paper detailing a new data storage format for ML. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Ali Ramlaoui, Daniel T. Speckhard, Sagar Pal, Fragkiskos D. Malliaros, Alexandre Duval, Victor Schmidt ·

    Atompack: A Storage and Distribution Layer for Read-Heavy Atomistic ML Training Datasets

    arXiv:2606.29975v1 Announce Type: new Abstract: Atomistic machine learning datasets are increasingly used for training: large immutable snapshots are read repeatedly, shuffled across epochs, staged across clusters' storage systems, and republished as reusable scientific artifacts…

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