AutoREC is an open‑source Python platform that uses reinforcement learning to automatically generate equivalent circuit models (ECMs) from electrochemical impedance spectroscopy (EIS) data. The platform frames ECM generation as a Markov decision process, training a Double Deep Q‑Network agent that iteratively modifies circuit topologies based on state, actions, and model feedback. It supports end‑to‑end workflows—including EIS preprocessing, agent training, ECM generation, and visualization—and has been demonstrated on synthetic datasets and real experimental spectra from batteries, corrosion, oxygen evolution, and CO₂ reduction systems.
By Ali Jaberi (Clean Energy Innovation Research Centre, National Research Council Canada, Mississauga, ON, Canada), Yonatan Kurniawan (Department of Materials Science and Engineering, University of Toronto, Toronto, ON, Canada), Robert Black (Clean Energy Innovation Research Centre, National Research Council Canada, Mississauga, ON, Canada), Shayan Mousavi M. (Clean Energy Innovation Research Centre, National Research Council Canada, Mississauga, ON, Canada), Kabir Verma (Cheriton School of Computer Science, University of Waterloo, Waterloo, ON, Canada), Zoya Sadighi (Clean Energy Innovation Research Centre, National Research Council Canada, Mississauga, ON, Canada), Santiago Miret (Lila Sciences, San Francisco, CA, USA), Jason Hattrick-Simpers (Department of Materials Science and Engineering, University of Toronto, Toronto, ON, Canada)
The paper introduces SO(3) Equivariant Neural Kalman Networks (SENK), a model that predicts vibrational spectra by cascading an equivariant transformer backbone, an Equivariant Neural Kalman bridge, and an NBO-informed electronic-prior pathway. SENK improves accuracy over DetaNet on QM9S and QMe14S datasets while maintaining full-spectrum IR and Raman fidelity from small molecules to drug-like systems. It also remains stable and selectively enhances spectrally sensitive features in biomolecular systems with complex stereoelectronic effects, enabling transferable vibrational spectroscopy across diverse molecular materials.
By Zetong Li, Zhuosong Xie, Hengyu Fan, Jiaao Yu, Qiyao Hua, Zheng Lu, Liming Xu, Juanni Wu, Honglin Li
arXiv:2607. 11943v1 Announce Type: cross Abstract: Long-horizon physics-based simulations of battery degradation provide mechanistic insight but remain computationally expensive, limiting their use for dense exploration of operating conditions over extended cycle life.
By Raghvender Raghvender, Mahdi Abid, Ferran Brosa Planella, Charles Delacourt, Arnaud Demorti\`ere
arXiv:2607. 18330v1 Announce Type: cross Abstract: The deployment of reliable lithium-ion battery management systems is crucial for accelerating electrification, yet the joint prognosis of State of Health (SOH) and Remaining Useful Life (RUL) remains severely hindered by task heteroscedasticity.
By Shuhao Chen, Tianyu Shi, Chengyi Tu
arXiv:2609.39340v1 Announce Type: new
Abstract: Electrolyte formulation design requires balancing multiple physicochemical properties, yet existing models often focus on a limited subset. Learning ea...
By Jiaxin Yu, Shuo Wang, Peng Wang, Yongcai Wang, Deying Li
arXiv:2606. 28220v1 Announce Type: new Abstract: Physics-informed neural networks (PINNs) have emerged as a powerful tool for solving nonlinear partial differential equations (PDEs), including battery electrochemical models.
By Gift Modekwe, Qiugang Lu
arXiv:2607. 14486v1 Announce Type: cross Abstract: Machine-learning force fields (MLFFs) are reliable only near their training distribution, making efficient construction of diverse training sets a major bottleneck for both train-from-scratch and foundation fine-tuning workflows.
By Sheng Bi, Yi-Ze Wang, Jun Cheng
arXiv:2607. 29095v1 Announce Type: new Abstract: Accurate prediction of lithium-ion battery state of health (SOH) is essential for reliable energy storage operation.
By Zeping Chen, Ruda Jian, Sachin Sigdel, Guoping Xiong, Jian-Xun Wang, Tengfei Luo
arXiv:2511. 05879v5 Announce Type: replace-cross Abstract: Hydrogen crossover is a critical safety and efficiency constraint in high-pressure polymer electrolyte membrane water electrolysis (PEMWE), but accurate prediction remains difficult because data are limited, transport physics are strongly coupled, and industrial operation requires reliable extrapolation beyond observed conditions.
By Yong-Woon Kim, Jihyeok Lee, Chulung Kang, Yung-Cheol Byun
The paper presents iPINN, an inverse physics‑informed neural network designed for broadband coherent anti‑Stokes Raman spectroscopy (BCARS) phase retrieval. iPINN predicts Lorentzian peak parameters from raw BCARS spectra and reconstructs the resonant susceptibility using a differentiable analytical forward model, employing a transformer encoder and a multi‑view consistency loss to handle varying non‑resonant background conditions. On a public benchmark it achieves the lowest mean absolute error (0.016) compared to other methods, and demonstrates depth‑invariant accuracy across multiple solvents and focal positions.
By Ravi Teja Vulchi, Carl Messerschmidt, Mohammadsadegh Vafaeinezhad, Rajendhar Junjuri, Tobias Meyer-Zedler, Juergen Popp, Thomas Bocklitz
arXiv:2507.09001v4 Announce Type: replace-cross
Abstract: Machine learning (ML) models for electronic structure typically rely on large datasets generated by computationally expensive Kohn-Sham densi...
By Sazzad Hossain, Ponkrshnan Thiagarajan, Shashank Pathrudkar, Stephanie Taylor, Abhijeet S. Gangan, Amartya S. Banerjee, Susanta Ghosh
arXiv:2609.21932v1 Announce Type: new
Abstract: Joint remaining useful life (RUL) prediction and capacity estimation require representations of both gradual degradation and recent battery behavior. T...
By Khoa Tran, Ho-Si-Hung Nguyen, Phone Wai Yan Moe, Hung-Cuong Trinh, Thi-Hoang-Giang Tran