Copertina

Computational Insights into PEMFC Durability: Degradation Mechanisms, Interfacial Chemistry, and the Emerging Role of Machine Learning Potentials

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Hinsch, Jack Jon, Fujimoto, Kazushi
Natura:	Preprint
Pubblicazione:	2026
Soggetti:	Materials Science Chemical Physics
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Benchmarking Universal Machine-Learned Interatomic Potentials for High-Temperature Metal-Organic Framework Chemistry
di: Edwards, Connor W., et al.
Pubblicazione: (2026)

Machine Learning Potentials for Heterogeneous Catalysis
di: Omranpour, Amir, et al.
Pubblicazione: (2024)

Efficient Grand Canonical Global Optimization with On-the-fly-trained Machine-learning Interatomic Potentials
di: Dominguez, Jon Eunan Quinlivan, et al.
Pubblicazione: (2025)

Atomistic Simulations of Oxide-Water Interfaces using Machine Learning Potentials
di: Elsner, Jan, et al.
Pubblicazione: (2025)

Dynamic Training Enhances Machine Learning Potentials for Long-Lasting Molecular Dynamics
di: Žugec, Ivan, et al.
Pubblicazione: (2025)

Machine Learned Potential for High-Throughput Phonon Calculations of Metal-Organic Frameworks
di: Elena, Alin Marin, et al.
Pubblicazione: (2024)

INN-FF: A Scalable and Efficient Machine Learning Potential for Molecular Dynamics
di: Mehereen, Taskin, et al.
Pubblicazione: (2025)

Constant-Potential Machine Learning Molecular Dynamics Simulations Reveal Potential-Regulated Cu Cluster Formation on MoS$_{2}$
di: Zhou, Jingwen, et al.
Pubblicazione: (2024)

Machine Learning and Molecular Simulations Reveal Mechanisms of ZIFs Polymorph Selection
di: Méndez, Emilio, et al.
Pubblicazione: (2026)

Accelerating Complex Materials Discovery with Universal Machine-Learning Potential-Driven Structure Prediction
di: An, Yuqi, et al.
Pubblicazione: (2026)

Expanding Universal Machine Learning Interatomic Potentials to 97 Elements Towards Nuclear Applications
di: Kuroda, Naoya, et al.
Pubblicazione: (2026)

Enhancing the Quality and Reliability of Machine Learning Interatomic Potentials through Better Reporting Practices
di: Maxson, Tristan, et al.
Pubblicazione: (2024)

Thermal Degradation Mechanisms and Stability Enhancement Strategies in Perovskite Solar Cells: A Review
di: Paul, Arghya, et al.
Pubblicazione: (2025)

Revealing Hydroxide Ion Transport Mechanisms in Commercial Anion-Exchange Membranes at Nano-Scale from Machine-learned Interatomic Potential Simulations
di: Hänseroth, Jonas, et al.
Pubblicazione: (2026)

Modeling the Behavior of Complex Aqueous Electrolytes Using Machine Learning Interatomic Potentials: The Case of Sodium Sulfate
di: Soyemi, Ademola, et al.
Pubblicazione: (2025)

Microscopic Structure and Dynamics of Interfacial Water at Fluorinated vs Nonfluorinated Surfaces -- Insights from Ab-Initio Simulations and IR Spectroscopy
di: Becker, Maximilian R., et al.
Pubblicazione: (2026)

Explicit Electric Potential-Embedded Machine Learning Framework: A Unified Description from Atomic to Electronic Scales
di: Zhou, Jingwen, et al.
Pubblicazione: (2026)

Characterizing Defect Dynamics in Silicon Carbide Using Symmetry-Adapted Collective Variables and Machine Learning Interatomic Potentials
di: Dutta, Soumajit, et al.
Pubblicazione: (2025)

Benchmarking Universal Machine Learning Interatomic Potentials for Supported Nanoparticles: Decoupling Energy Accuracy from Structural Exploration
di: Xu, Jiayan, et al.
Pubblicazione: (2025)

Structural Insights and Advanced Spectroscopic Characterization of Thiazolothiazoles: Unveiling Potential for Optoelectronic and Sensing Applications
di: Gutmańska, Karolina, et al.
Pubblicazione: (2025)

Electrochemical Interfaces at Constant Potential: Data-Efficient Transfer Learning for Machine-Learning-Based Molecular Dynamics
di: Bianchi, Michele Giovanni, et al.
Pubblicazione: (2025)

Fragment-Constrained Charge Equilibration for Charge-Aware Machine Learning Potentials at Electrochemical Interfaces
di: Peeketi, Akhil Reddy, et al.
Pubblicazione: (2026)

Optimizing Machine Learning Potentials for Hydroxide Transport: Surprising Efficiency of Single-Concentration Training
di: Hänseroth, Jonas, et al.
Pubblicazione: (2025)

Best Practices for Fitting Machine Learning Interatomic Potentials for Molten Salts: A Case Study Using NaCl-MgCl2
di: Attarian, Siamak, et al.
Pubblicazione: (2024)

Equivariant Machine Learning Interatomic Potentials with Global Charge Redistribution
di: Maruf, Moin Uddin, et al.
Pubblicazione: (2025)

Influence of Chemistry and Topography on the Wettability of Copper
di: Lößlein, Sarah Marie, et al.
Pubblicazione: (2025)

MOFClassifier: A Machine Learning Approach for Validating Computation-Ready Metal-Organic Frameworks
di: Zhao, Guobin, et al.
Pubblicazione: (2025)

Computer Simulation of the Growth of a Metal-Organic Framework Proto-crystal at Constant Chemical Potential
di: Gargari, Sahar Andarzi, et al.
Pubblicazione: (2025)

Accuracy and Limitations of Machine-Learned Interatomic Potentials for Magnetic Systems: A Case Study on Fe-Cr-C
di: Khazieva, E. O., et al.
Pubblicazione: (2025)

Insights into the Structure and Dynamics of Water at Co$_3$O$_4$(001) Using a High-Dimensional Neural Network Potential
di: Omranpour, Amir, et al.
Pubblicazione: (2025)

Unraveling the Corrosion Mechanism of Boro-Alumino-Phospho-Silicate Glass: Advanced Insights from Solid-State NMR Spectroscopy
di: Khan, Muhammad Amer, et al.
Pubblicazione: (2025)

Robustness of Local Predictions in Atomistic Machine Learning Models
di: Chong, Sanggyu, et al.
Pubblicazione: (2023)

Machine Learning Accelerated Computational Surface-Specific Vibrational Spectroscopy Reveals Oxidation Level of Graphene in Contact with Water
di: Du, Xianglong, et al.
Pubblicazione: (2025)

The Role of THz Phonons in the Ionic Conduction Mechanism of $Li_7La_3Zr_2O_{12}$ Polymorphs
di: Lin, Amy K., et al.
Pubblicazione: (2025)

Concerted Electron-Ion Transport by Polyacrylonitrile Elucidated with Reactive Deep Learning Potentials
di: Chahal-Crockett, Rajni, et al.
Pubblicazione: (2026)

Matlantis-PFP v8: Universal Machine Learning Interatomic Potential with Better Experimental Agreements via r2SCAN Functional
di: Shinagawa, Chikashi, et al.
Pubblicazione: (2026)

Cutting Through the Noise: On-the-fly Outlier Detection for Robust Training of Machine Learning Interatomic Potentials
di: Lam, Terry C. W., et al.
Pubblicazione: (2026)

Unified Deep Learning Framework for Many-Body Quantum Chemistry via Green's Functions
di: Venturella, Christian, et al.
Pubblicazione: (2024)

Hybrid Quantum--Classical Machine Learning Potential with Variational Quantum Circuits
di: Willow, Soohaeng Yoo, et al.
Pubblicazione: (2025)

Macro-Dipole-Constrainted Learning of Atomic Charges for Accurate Electrostatic Potentials at Electrochemical Interfaces
di: Yang, Jing, et al.
Pubblicazione: (2025)