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PRX Intelligence

Accepted Papers

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 system collapse in 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

Sampling weights at intermediate temperatures is optimal for training large language protein models

L. Ghiringhelli, A. Zambon, and G. Tiana

Accepted 21 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

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

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

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

Transformer neural-network quantum states for lattice models of spins and fermions: Application to the ancilla layer model

Riccardo Rende, Alexander Nikolaenko, Luciano Loris Viteritti, Subir Sachdev, and Ya-Hui Zhang

Accepted 8 September, 2026

Fidelity of machine-learned potentials: Quantitative assessment for protonated oxalate

Chen Qu, Paul L. Houston, Qi Yu, Apurba Nandi, Joel M. Bowman, Valerii Andreichev, Silvan Käser, and Markus Meuwly

Accepted 2 September, 2026

Probabilistic denoising for reliable signal extraction in spectroscopy

Younsik Kim and Changyoung Kim

Accepted 31 August, 2026

: Transferring particle physics knowledge across the cosmos

Vinicius Mikuni, Ibrahim Elsharkawy, and Benjamin Nachman

Accepted 24 August, 2026

Scientific machine learning of chaotic systems learns reduced-order equations for neural populations

Anthony G. Chesebro, David Hofmann, Vaibhav Dixit, Earl K. Miller, Richard H. Granger, Alan Edelman, Christopher V. Rackauckas, Lilianne R. Mujica-Parodi, and Helmut H. Strey

Accepted 13 August, 2026

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