English(EN) Atom-JEPA: Joint-Embedding Predictive Architecture for 3D Atomistic Systems

Atom-JEPA框架推进了3D原子系统的自监督学习

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-07 04:00

研究人员开发了Atom-JEPA，一个新颖的自监督预训练框架，旨在提高机器学习模型在3D原子系统上的泛化能力。受联合嵌入预测架构的启发，Atom-JEPA利用互补的原子级和子结构级目标，从无标签的结构数据中学习潜在表示。在广泛的分子和晶体数据集上进行预训练后，Atom-JEPA在分子ADMET和量子化学性质预测任务上展现了最先进的性能，并在预测晶体材料性质方面也取得了优异的成果。 AI

影响增强了处理3D结构数据的AI模型的泛化能力，有望加速材料科学和药物研发的发现。

排序理由该集群包含一篇详细介绍原子系统新机器学习框架的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

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Newsworthiness bucket: Tool; 该集群包含一篇详细介绍原子系统新机器学习框架的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]
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arXiv cs.AI TIER_1 English(EN) · Kasper Helverskov Petersen, Rasmus Hannibal Tirsgaard, Fran\c{c}ois R J Cornet, Mikkel Jordahn, Mikkel N. Schmidt · 2026-10-07 04:00

Atom-JEPA：3D原子系统的联合嵌入预测架构

arXiv:2610.08400v1 Announce Type: cross Abstract: Large-scale self-supervised pretraining has reshaped modern machine learning, substantially advancing the ability of language and vision models to generalize across downstream tasks. While deep learning has driven considerable pro…

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Atom-JEPA：3D原子系统的联合嵌入预测架构

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