arXiv Machine Learning July 9, 2026 By Siqi Chen, Zhiqiang Wang, Yili Shen, Xianqi Deng, Xi Cheng, Cheng-Wei Ju, Jun Yi, Guo Ling, Dieaa Alhmoud, Hui Guan, Zhou Lin

Transferable FB-GNN-MBE Framework for Potential Energy Surfaces: Data-Adaptive Transfer Learning in Deep Learned Many-Body Expansion Theory

Read the original on arXiv Machine Learning →

arXiv:2604. 09320v2 Announce Type: replace-cross Abstract: Mechanistic understanding and rational design of complex chemical systems depend on fast and accurate predictions of electronic structures beyond individual building blocks.

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

arXiv AI

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Dual-Level Atomic and Coordination Geometry Learning for Crystal Property Prediction Using Graph Neural Networks

arXiv:2607. 24818v1 Announce Type: cross Abstract: Accurate prediction of crystal properties remains a key challenge in computational materials science.

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arXiv Machine Learning

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arXiv:2503. 19847v2 Announce Type: replace-cross Abstract: The accurate quantum chemical calculation of excited states is a challenging task, often requiring computationally demanding methods.

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arXiv Machine Learning

Jul 1

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

arXiv:2606. 30961v1 Announce Type: cross Abstract: Advances in deep learning architectures and representations have enabled ML-driven chemical property prediction, but state-of-the-art (SOTA) models have remained largely confined to independent codebases and lack support for diverse chemical species.

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arXiv Machine Learning

Jul 8

Multimodal Molecular Representation Learning with Graph Neural Networks, Deep & Cross Networks, and SMILES Embeddings

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arXiv Machine Learning

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PRIME: Protein Representation via Physics-Informed Multiscale Equivariant Hierarchies

arXiv:2605. 01625v3 Announce Type: replace Abstract: Proteins are inherently multiscale physical systems whose functional properties emerge from coordinated structural organization across multiple spatial resolutions, ranging from atomic interactions to global fold topology.

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arXiv Machine Learning

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Shoot from the HIP: Hessian Interatomic Potentials without derivatives

arXiv:2509. 21624v3 Announce Type: replace Abstract: Fundamental tasks in computational chemistry, from transition state search to vibrational analysis, rely on molecular Hessians, which are the second derivatives of the potential energy.

By Andreas Burger, Luca Thiede, Nikolaj R{\o}nne, Varinia Bernales, Nandita Vijaykumar, Tejs Vegge, Arghya Bhowmik, Alan Aspuru-Guzik

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Dual-Level Atomic and Coordination Geometry Learning for Crystal Property Prediction Using Graph Neural Networks

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