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New method optimizes training data for machine-learned interatomic potentials

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new method for selecting training data for machine-learned interatomic potentials, which are crucial for simulating materials at the atomic level. The study introduces a budget-dependent crossover strategy that balances structural diversity with targeting configurations where models show disagreement. This approach was tested on MACE models using GAP-20 Carbon and MD17 datasets, demonstrating that the optimal selection method depends on the amount of data retained. AI

IMPACT This research could lead to more efficient and accurate simulations of materials, accelerating discovery in fields like chemistry and physics.

RANK_REASON The cluster contains an academic paper detailing a new methodology in machine learning for materials science. [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 method optimizes training data for machine-learned interatomic potentials

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The cluster contains an academic paper detailing a new methodology in machine learning for materials science. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Jia Bi, Alin-Marin Elena ·

    A budget-dependent crossover between coverage- and response-based training-set selection for machine-learned interatomic potentials

    arXiv:2609.05877v1 Announce Type: new Abstract: Selecting compact training sets for machine-learned interatomic potentials requires deciding whether to preserve structural diversity or target configurations on which models disagree. The better choice can depend on how much data i…

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