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NEMORA: Neural Equivariant Multipole Operators for Long-Range Atomistic Learning

By PulseAugur Editorial · [1 sources] ·

Researchers have developed NEMORA, a novel neural equivariant extension of the Fast Multipole Method designed for long-range atomistic learning. This method generalizes the analytical multipole expansion and translation operators to learned equivariant counterparts, enabling it to capture many-body interactions across various length scales while maintaining linear time and memory complexity. NEMORA can handle systems with hundreds of thousands of atoms and significantly reduces force and energy errors compared to short-range models, outperforming or matching existing long-range extensions in accuracy. AI

IMPACT NEMORA's efficient handling of long-range interactions could accelerate the development of more accurate and scalable machine learning models for atomistic simulations.

RANK_REASON The cluster contains an academic paper detailing a new method 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 →

NEMORA: Neural Equivariant Multipole Operators for Long-Range Atomistic Learning

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The cluster contains an academic paper detailing a new method 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) · Jay L. Kaplan, Samuel Varner, Rebecca Willett, Juan J. de Pablo ·

    NEMORA: Neural Equivariant Multipole Operators for Long-Range Atomistic Learning

    arXiv:2610.10776v1 Announce Type: new Abstract: Equivariant graph neural networks have emerged as foundational architectures for machine-learned interatomic potentials, approaching quantum-chemical accuracy at a fraction of the computational cost. These models describe local atom…

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