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Atom-JEPA framework advances self-supervised learning for 3D atomistic systems

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Researchers have developed Atom-JEPA, a novel self-supervised pretraining framework designed to improve the generalization capabilities of machine learning models for 3D atomistic systems. Inspired by joint-embedding predictive architectures, Atom-JEPA utilizes complementary atom-level and substructure-level objectives to learn latent representations from unlabeled structural data. When pre-trained on extensive molecular and crystalline datasets, Atom-JEPA demonstrated state-of-the-art performance on molecular ADMET and quantum-chemical property prediction tasks, while also showing strong results in predicting crystalline material properties. AI

IMPACT Enhances generalization for AI models processing 3D structural data, potentially accelerating discovery in materials science and drug development.

RANK_REASON The cluster contains a research paper detailing a new machine learning framework for atomistic systems. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Atom-JEPA framework advances self-supervised learning for 3D atomistic systems

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The cluster contains a research paper detailing a new machine learning framework for atomistic systems. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. 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 ·

    Atom-JEPA: Joint-Embedding Predictive Architecture for 3D Atomistic Systems

    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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