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English(EN) Mixture of experts architectures for machine learning interatomic potentials

新的MoE架构提升了MLIPs的性能和可解释性

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员为机器学习原子间势（MLIPs）开发了新的专家混合（MoE）和线性专家混合（MoLE）架构。这些模型集成到DPA3框架中，在OMol25、OMat24和OC20M等基准测试中，与现有基线相比，性能有了显著提升。研究强调，共享专家和逐元素路由策略的稀疏激活是增强模型容量和实现化学上可解释的专家专业化的关键。 AI

影响 为MLIPs引入了新颖的架构，有望提高原子模拟的准确性和可解释性。

排序理由 详细介绍新模型架构和基准测试结果的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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新的MoE架构提升了MLIPs的性能和可解释性

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详细介绍新模型架构和基准测试结果的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Yuzhi Liu, Duo Zhang, Anyang Peng, Weinan E, Linfeng Zhang, Han Wang ·

    用于机器学习原子间势的专家混合架构

    arXiv:2603.07977v3 Announce Type: replace-cross Abstract: Machine Learning Interatomic Potentials (MLIPs) enable accurate large-scale atomistic simulations, yet improving their expressive capacity efficiently remains challenging. Here we systematically investigate Mixture-of-Expe…

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