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. This workload differs from interactive scientific curation, where mutable records and ad hoc inspection are often more important than random indexed throughput. We present Atompack, an append-oriented storage format and distribution layer designed around a simple workload: training pipelines usually consume complete
The proliferation of atomistic machine learning necessitates specialized data handling solutions to manage increasingly large and complex datasets efficiently.
Efficient data storage and distribution are critical infrastructure for accelerating ML research and development in materials science and other atomic-level applications, directly impacting AI training capabilities.
The introduction of a specialized storage format like Atompack streamlines the training pipeline for atomistic ML, potentially lowering computational barriers and speeding up discovery.
- · AI/ML researchers
- · Materials science
- · Cloud storage providers
- · Data infrastructure developers
- · Generic data storage solutions
- · Inefficient ML training workflows
Atomistic ML training becomes more efficient and scalable due to optimized data handling.
Faster iteration cycles in materials discovery and AI model development lead to new breakthroughs.
Enhanced atomistic ML capabilities contribute to advancements in areas like battery technology, catalysts, and drug discovery, impacting various industrial sectors.
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Read at arXiv cs.LG