arXiv Machine Learning July 23, 2026 By Beom Seok Kang, Vignesh C. Bhethanabotla, Amin Tavakoli, Maurice D. Hanisch, Arimitsu Horikawa-Strakovsky, Miguel Nouman, Danish Khan, William A. Goddard III, Anima Anandkumar

OrbitAll: A Unified Quantum Mechanical Representation Deep Learning Framework for All Molecular Systems

Read the original on arXiv Machine Learning →

arXiv:2507. 03853v2 Announce Type: replace Abstract: We introduce OrbitAll, a geometry- and physics-informed deep learning framework that encodes any molecular system with arbitrary charges, spins, and environmental effects using electronic structure information.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv Machine Learning

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ElemeNet: Multiscale Molecular Machine Learning with Uncertainty Quantification Across the Periodic Table

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arXiv AI

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arXiv Machine Learning

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arXiv Machine Learning

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arXiv Machine Learning

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arXiv:2602. 16908v2 Announce Type: replace-cross Abstract: Allegro is a machine learning interatomic potential model designed to predict atomic properties in molecules using E(3) equivariant neural networks.

By G. Laskaris, D. Morozov, D. Tarpanov, A. Seth, J. Procelewska, G. Sai Gautam, A. Sagingalieva, R. Brasher, A. Melnikov

arXiv Machine Learning

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arXiv:2605. 16823v2 Announce Type: replace Abstract: Large language models succeed by combining large-scale pretraining with meaningful discrete tokens.

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ElemeNet: Multiscale Molecular Machine Learning with Uncertainty Quantification Across the Periodic Table

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Shoot from the HIP: Hessian Interatomic Potentials without derivatives

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