English(EN) BranchIP: Learning Adaptive Equivariant Computation for Interatomic Potentials

BranchIP框架通过自适应计算加速MLIPs

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-03 04:00

研究人员推出BranchIP，一个旨在提高机器学习原子势能（MLIPs）效率的新型框架。该新模型采用自适应张量积计算，该技术根据化学复杂性和动力学优化计算深度。实验表明，BranchIP可将MLIPs模拟速度提高2.4倍，内存使用量减少2.6倍，同时保持对复杂材料系统的准确性。BranchIP的自适应性还揭示了哪些相互作用需要更密集的计算，从而提高了模型的可解释性。 AI

影响引入了一种显著加速原子模拟并减少MLIPs内存需求的方法，有望在材料科学中实现更大规模和更长时间的模拟。

排序理由这是一篇详细介绍MLIPs新计算框架的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Laura Zichi, Gil Harari, Chuin Wei Tan, Marc L. Descoteaux, Albert Zhu, Menghang Wang, Yoel Zimmermann, H. T. Kung, Boris Kozinsky · 2026-10-03 04:00

BranchIP：为原子间势能学习自适应等变计算

arXiv:2610.02013v1 Announce Type: new Abstract: Equivariant machine learning interatomic potentials (MLIPs) have revolutionized atomistic modeling, but accurate treatment of complex materials and molecular systems demands expensive models. This limits simulation length- and time-…

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报道来源 [1]

BranchIP：为原子间势能学习自适应等变计算

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