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New framework enables scalable, robust active learning for MLIPs

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new active learning framework for machine-learning interatomic potentials (MLIPs) that addresses scalability and robustness challenges. This framework utilizes a force-aware Neural Tangent Kernel (NTK) to efficiently screen large candidate pools of molecular structures. The method demonstrates effectiveness on the OC20 dataset, achieving low energy and force errors, and remains competitive and robust on other benchmarks. AI

IMPACT Introduces a more efficient and robust method for training interatomic potentials, potentially accelerating materials science discovery.

RANK_REASON Publication of an academic paper detailing a new methodology for machine learning. [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 framework enables scalable, robust active learning for MLIPs

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Publication of an academic paper detailing a new methodology for machine learning. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Shikha Surana ·

    Force-Aware Neural Tangent Kernels for Scalable and Robust Active Learning of MLIPs

    Active learning for machine-learning interatomic potentials (MLIPs) must address several challenges to be practical: scaling to large candidate pools, leveraging energy-force supervision, and maintaining robustness when candidate pools are biased relative to the target distributi…

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