Accepted Papers

Discovering interpretable ICF 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 AI 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

Can Transformers predict crises and collapse in nonlinear dynamical systems?

Zheng-Meng Zhai, Celso Grebogi, and Ying-Cheng Lai

Accepted 2 October, 2026

Tracking large chemical reaction networks and rare events by neural networks

Jiayu Weng, Xinyi Zhu, Jing Liu, Linyuan Lü, Pan Zhang, and Ying Tang

Accepted 24 September, 2026

Achieving robust extrapolation in materials property prediction via decoupled transfer learning

Tasuku Sugiura and Teruyasu Mizoguchi

Accepted 24 September, 2026

Machine-learned particle flow as a foundation model for collider physics

Farouk Mokhtar, Joosep Pata, Michael Kagan, and Javier Duarte

Accepted 17 September, 2026

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

Ezequiel Alvarez, Sean Benevedes, Manuel Szewc, and Jesse Thaler

Accepted 17 September, 2026

Learning activator-inhibitor dynamics at the cell cortex with neural likelihood ratio estimation

Ondrej Maxian, Edwin Munro, and Aaron R. Dinner

Accepted 16 September, 2026

Achieving angular-momentum conservation with physics-informed neural networks in computational relativistic spin hydrodynamics

Hidefumi Matsuda, Koichi Hattori, and Koichi Murase

Accepted 16 September, 2026

LARA-HPC: Validation-driven agentic supercomputer workflows for atomistic modeling

William Dawson, Louis Beal, Yoann Curé, Giuseppe Fisicaro, Dorian Rolland, and Luigi Genovese

Accepted 15 September, 2026

Detailed balance in large-language-model-driven agents

Zhuo-Yang Song, Qing-Hong Cao, Ming-xing Luo, and Hua Xing Zhu

Accepted 15 September, 2026

Dressing composite fermions with artificial intelligence

Mytraya Gattu

Accepted 15 September, 2026

Interpretable artificial intelligence analysis of strongly correlated electrons

Changkai Zhang and Jan von Delft

Accepted 15 September, 2026

Stochastic thermodynamics for autoregressive generative models: A non-Markovian perspective

Takahiro Sagawa

Accepted 10 September, 2026

Attention is not all you need for diffraction

Elizabeth J. Baggett, Edward G. Friedman, Abhishek Shetty, Derrick Chan-Sew, Vanellsa Acha, Harshita Dwarcherla, Paul Kienzle, and William Ratcliff

Accepted 9 September, 2026

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Accepted Papers

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Discovering interpretable ICF 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 AI 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

Machine-learned particle flow as a foundation model for collider physics

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

Learning activator-inhibitor dynamics at the cell cortex with neural likelihood ratio estimation

Achieving angular-momentum conservation with physics-informed neural networks in computational relativistic spin hydrodynamics

LARA-HPC: Validation-driven agentic supercomputer workflows for atomistic modeling

Detailed balance in large-language-model-driven agents

Dressing composite fermions with artificial intelligence

Interpretable artificial intelligence analysis of strongly correlated electrons

Stochastic thermodynamics for autoregressive generative models: A non-Markovian perspective

Attention is not all you need for diffraction

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