Accepted Papers

Molecular Hamiltonian learning from setpoint-dependent scanning tunneling spectroscopy

Greta Lupi, Adolfo O. Fumega, Mohammad Amini, Robert Drost, Peter Liljeroth, and Jose L. Lado

Accepted 6 November, 2026

Distilling latent electrostatics from foundation machine learning interatomic potentials

Xiaoyu Wang and Bingqing Cheng

Accepted 6 November, 2026

Quantum geometry of data

Alexander G. Abanov, Luca Candelori, Harold C. Steinacker, Martin T. Wells, Jerome R. Busemeyer, Cameron J. Hogan, Vahagn Kirakosyan, Nicola Marzari, Sunil Pinnamaneni, Dario Villani, Mengjia Xu, and Kharen Musaelian

Accepted 5 November, 2026

Understanding and embracing imperfection in physical learning networks

Sam Dillavou, Marcelo Guzman, Andrea J. Liu, and Douglas J. Durian

Accepted 4 November, 2026

Decoding molecular geometries in Coulomb explosion imaging via physics-informed deep neural network

Xingyu Guo, Enliang Wang, Wenguang Wu, Zhaopeng Xing, Tuo Liu, Chunkai Xu, Xu Shan, Artem Rudenko, Daniel Rolles, Jing Chen, and Xiangjun Chen

Accepted 4 November, 2026

Bayesian quantum sensing using graybox machine learning

Akram Youssry, Stefan Todd, Patrick Murton, Muhammad Junaid Arshad, Nicholas Werren, Alberto Peruzzo, and Cristian Bonato

Accepted 29 October, 2026

Constraint-aware functional cloning for stable and transferable machine-learned density functional theory

Sara Navarro-Rodríguez, Alec Wills, Kimberly J. Daas, María Camarasa-Gómez, and Marivi Fernández-Serra

Accepted 28 October, 2026

Critical slowing down in training diffusion models

Luca Maria Del Bono, Giulio Biroli, Patrick Charbonneau, and Marylou Gabrié

Accepted 28 October, 2026

Discovering interpretable inertial confinement fusion hot spot models with deep learning

M. Pokornik, K. Humbird, R. C. Nora, and O. Hurricane

Accepted 23 October, 2026

Direct inference of linear viscoelastic memory from chirp rheometry via physics-informed Gaussian processes

Isaac Y. Miranda-Valdez, Juha Koivisto, and Mikko J. Alava

Accepted 19 October, 2026

False metallization in short-ranged machine learned interatomic potentials

Isaac J. Parker, Mandy J. Hoffmann, William J. Baldwin, Shuang Han, Srishti Gupta, Kara D. Fong, Angelos Michaelides, Christoph Schran, Sandip De, and Gábor Csányi

Accepted 19 October, 2026

Lean-verified discovery of exact nonadditive quantum codes with artificial intelligence assistants

Xi He, Sirui Lu, and Bei Zeng

Accepted 6 October, 2026

Strain-induced bcc-to-fcc transformation in tungsten uncovered by molecular dynamics simulations with machine-learned potentials

Qiu-Hong Hu, YueYue Wang, Xinyi Leng, Hezhu Shao, and Luru Dai

Accepted 5 October, 2026

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Accepted Papers

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Molecular Hamiltonian learning from setpoint-dependent scanning tunneling spectroscopy

Distilling latent electrostatics from foundation machine learning interatomic potentials

Quantum geometry of data

Understanding and embracing imperfection in physical learning networks

Decoding molecular geometries in Coulomb explosion imaging via physics-informed deep neural network

Bayesian quantum sensing using graybox machine learning

Constraint-aware functional cloning for stable and transferable machine-learned density functional theory

Critical slowing down in training diffusion models

Discovering interpretable inertial confinement fusion hot spot models with deep learning

Direct inference of linear viscoelastic memory from chirp rheometry via physics-informed Gaussian processes

False metallization in short-ranged machine learned interatomic potentials

Lean-verified discovery of exact nonadditive quantum codes with artificial intelligence assistants

Strain-induced bcc-to-fcc transformation in tungsten uncovered by molecular dynamics simulations with machine-learned potentials

Can Transformers predict crises and collapse in nonlinear dynamical systems?

Tracking large chemical reaction networks and rare events by neural networks

Achieving robust extrapolation in materials property prediction via decoupled transfer learning

Many wrongs make a right: Leveraging biased simulations toward unbiased parameter inference

Attention is not all you need for diffraction

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