A Machine-Learning Accelerated Grand Canonical Sampling Framework for Nuclear Quantum Effects in Constant Potential Electrochemistry
Fuente:
arXiv
Guardado en:
| Autores principales: | Sun, Menglin, Jin, Bin, Yang, Xiaolong, Xu, Shenzhen |
|---|---|
| Formato: | Preprint |
| Publicado: |
2024
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
Electrochemistry-Enhanced Dynamic Paths Sampling Unveiling Nuclear Quantum Effects in Electrocatalysis
por: Fu, Li, et al.
Publicado: (2025)
por: Fu, Li, et al.
Publicado: (2025)
Materials Acceleration Platform for Electrochemistry: a Platform for Autonomous Electrochemistry
por: Persaud, Daniel, et al.
Publicado: (2026)
por: Persaud, Daniel, et al.
Publicado: (2026)
Observing Nucleation and Crystallization of Rocksalt LiF from Molten State through Molecular Dynamics Simulations with Refined Machine-Learned Force Field
por: Xu, Boyuan, et al.
Publicado: (2025)
por: Xu, Boyuan, et al.
Publicado: (2025)
Efficient Grand Canonical Global Optimization with On-the-fly-trained Machine-learning Interatomic Potentials
por: Dominguez, Jon Eunan Quinlivan, et al.
Publicado: (2025)
por: Dominguez, Jon Eunan Quinlivan, et al.
Publicado: (2025)
Observing Li Nucleation at Li Metal-Solid Electrolyte Interface in All-Solid-State Batteries
por: An, Yun, et al.
Publicado: (2024)
por: An, Yun, et al.
Publicado: (2024)
Quantum-Accurate Machine Learning Potentials for Metal-Organic Frameworks using Temperature Driven Active Learning
por: Sharma, Abhishek, et al.
Publicado: (2024)
por: Sharma, Abhishek, et al.
Publicado: (2024)
Interstellar Dust-Catalyzed Molecular Hydrogen Formation Enabled by Nuclear Quantum Effects
por: Yang, Xiaolong, et al.
Publicado: (2025)
por: Yang, Xiaolong, et al.
Publicado: (2025)
Accounting for the Quantum Capacitance of Graphite in Constant Potential Molecular Dynamics Simulations
por: Goloviznina, Kateryna, et al.
Publicado: (2024)
por: Goloviznina, Kateryna, et al.
Publicado: (2024)
MLIP-MC: A Framework for Adsorption Simulations using Machine-Learned Interatomic Potentials
por: Edwards, Connor W., et al.
Publicado: (2026)
por: Edwards, Connor W., et al.
Publicado: (2026)
Bias in Universal Machine-Learned Interatomic Potentials and its Effects on Fine-Tuning
por: Wong, Nicolas, et al.
Publicado: (2026)
por: Wong, Nicolas, et al.
Publicado: (2026)
Accelerating High-Throughput Phonon Calculations via Machine Learning Universal Potentials
por: Lee, Huiju, et al.
Publicado: (2024)
por: Lee, Huiju, et al.
Publicado: (2024)
Stability and Structure of Binary Metal Hydrides under Pressure, Electrochemical Potential and Combined Pressure-Electrochemistry
por: Phuthi, Mgcini Keith, et al.
Publicado: (2025)
por: Phuthi, Mgcini Keith, et al.
Publicado: (2025)
Expanding Universal Machine Learning Interatomic Potentials to 97 Elements Towards Nuclear Applications
por: Kuroda, Naoya, et al.
Publicado: (2026)
por: Kuroda, Naoya, et al.
Publicado: (2026)
Constant-Potential Machine Learning Molecular Dynamics Simulations Reveal Potential-Regulated Cu Cluster Formation on MoS$_{2}$
por: Zhou, Jingwen, et al.
Publicado: (2024)
por: Zhou, Jingwen, et al.
Publicado: (2024)
Machine-Learned Atomic Cluster Expansion Potentials for Fast and Quantum-Accurate Thermal Simulations of Wurtzite AlN
por: Yang, Guang, et al.
Publicado: (2023)
por: Yang, Guang, et al.
Publicado: (2023)
Nuclear Quantum Effects on Proton Diffusivity in Perovskite Oxides
por: Yamada, Shunya, et al.
Publicado: (2024)
por: Yamada, Shunya, et al.
Publicado: (2024)
Symplectic Spin-Lattice Dynamics with Machine-Learning Potentials
por: Huang, Zhengtao, et al.
Publicado: (2025)
por: Huang, Zhengtao, et al.
Publicado: (2025)
Accelerating Amorphous Alloy Discovery: Data-Driven Property Prediction via General-Purpose Machine Learning Interatomic Potential
por: Gong, Xuhe, et al.
Publicado: (2025)
por: Gong, Xuhe, et al.
Publicado: (2025)
Coordination Engineering of Dual-Atom Catalysts for Overall Water Splitting: Mechanistic Insights from Constant-Potential First-Principles and Machine Learning
por: Li, Jiahang, et al.
Publicado: (2026)
por: Li, Jiahang, et al.
Publicado: (2026)
BEAST DB: Grand-Canonical Database of Electrocatalyst Properties
por: Tezak, Cooper, et al.
Publicado: (2024)
por: Tezak, Cooper, et al.
Publicado: (2024)
Electrochemical Interfaces at Constant Potential: Data-Efficient Transfer Learning for Machine-Learning-Based Molecular Dynamics
por: Bianchi, Michele Giovanni, et al.
Publicado: (2025)
por: Bianchi, Michele Giovanni, et al.
Publicado: (2025)
Accelerating Phonon Thermal Conductivity Prediction by an Order of Magnitude Through Machine Learning-Assisted Extraction of Anharmonic Force Constants
por: Srivastava, Yagyank, et al.
Publicado: (2024)
por: Srivastava, Yagyank, et al.
Publicado: (2024)
Symmetry-breaking strain drives significant reduction in lattice thermal conductivity: A case study of boron arsenide
por: Chen, Kaile, et al.
Publicado: (2025)
por: Chen, Kaile, et al.
Publicado: (2025)
Accelerating Complex Materials Discovery with Universal Machine-Learning Potential-Driven Structure Prediction
por: An, Yuqi, et al.
Publicado: (2026)
por: An, Yuqi, et al.
Publicado: (2026)
Small-Cell-Based Fast Active Learning of Machine Learning Interatomic Potentials
por: Meng, Zijian, et al.
Publicado: (2025)
por: Meng, Zijian, et al.
Publicado: (2025)
Hybrid Quantum--Classical Machine Learning Potential with Variational Quantum Circuits
por: Willow, Soohaeng Yoo, et al.
Publicado: (2025)
por: Willow, Soohaeng Yoo, et al.
Publicado: (2025)
A "Magnetic" Machine Learning Interatomic Potential for Nickel
por: Gong, Xiaoguo, et al.
Publicado: (2023)
por: Gong, Xiaoguo, et al.
Publicado: (2023)
Accelerating the Training and Improving the Reliability of Machine-Learned Interatomic Potentials for Strongly Anharmonic Materials through Active Learning
por: Kang, Kisung, et al.
Publicado: (2024)
por: Kang, Kisung, et al.
Publicado: (2024)
Ground-State Structure Search of Defective High-Entropy Alloys Using Machine-Learning Potentials and Monte Carlo Sampling
por: Zhu, Siya, et al.
Publicado: (2026)
por: Zhu, Siya, et al.
Publicado: (2026)
Universal Machine Learning Potentials under Pressure
por: Loew, Antoine, et al.
Publicado: (2025)
por: Loew, Antoine, et al.
Publicado: (2025)
Energy Underprediction from Symmetry in Machine-Learning Interatomic Potentials
por: Nong, Wei, et al.
Publicado: (2025)
por: Nong, Wei, et al.
Publicado: (2025)
Computer Simulation of the Growth of a Metal-Organic Framework Proto-crystal at Constant Chemical Potential
por: Gargari, Sahar Andarzi, et al.
Publicado: (2025)
por: Gargari, Sahar Andarzi, et al.
Publicado: (2025)
Universal Machine Learning Interatomic Potentials are Ready for Phonons
por: Loew, Antoine, et al.
Publicado: (2024)
por: Loew, Antoine, et al.
Publicado: (2024)
Uncertainty-Aware Machine-Learning Framework for Predicting Dislocation Plasticity and Stress-Strain Response in FCC Alloys
por: Luo, Jing, et al.
Publicado: (2025)
por: Luo, Jing, et al.
Publicado: (2025)
Accelerating the Search for Superconductors Using Machine Learning
por: Adiga, Suhas, et al.
Publicado: (2025)
por: Adiga, Suhas, et al.
Publicado: (2025)
Machine Learned Potential for High-Throughput Phonon Calculations of Metal-Organic Frameworks
por: Elena, Alin Marin, et al.
Publicado: (2024)
por: Elena, Alin Marin, et al.
Publicado: (2024)
Agentic AI and Machine Learning for Accelerated Materials Discovery and Applications
por: Chen, Jihua, et al.
Publicado: (2026)
por: Chen, Jihua, et al.
Publicado: (2026)
Uncertainty Quantification for Misspecified Machine Learned Interatomic Potentials
por: Perez, Danny, et al.
Publicado: (2025)
por: Perez, Danny, et al.
Publicado: (2025)
Classical and Machine Learning Interatomic Potentials for BCC Vanadium
por: Wang, Rui, et al.
Publicado: (2022)
por: Wang, Rui, et al.
Publicado: (2022)
Uni2D: A Universal Machine Learning Interatomic Potential for Two-Dimensional Materials
por: Wang, Haidi, et al.
Publicado: (2025)
por: Wang, Haidi, et al.
Publicado: (2025)
Ejemplares similares
-
Electrochemistry-Enhanced Dynamic Paths Sampling Unveiling Nuclear Quantum Effects in Electrocatalysis
por: Fu, Li, et al.
Publicado: (2025) -
Materials Acceleration Platform for Electrochemistry: a Platform for Autonomous Electrochemistry
por: Persaud, Daniel, et al.
Publicado: (2026) -
Observing Nucleation and Crystallization of Rocksalt LiF from Molten State through Molecular Dynamics Simulations with Refined Machine-Learned Force Field
por: Xu, Boyuan, et al.
Publicado: (2025) -
Efficient Grand Canonical Global Optimization with On-the-fly-trained Machine-learning Interatomic Potentials
por: Dominguez, Jon Eunan Quinlivan, et al.
Publicado: (2025) -
Observing Li Nucleation at Li Metal-Solid Electrolyte Interface in All-Solid-State Batteries
por: An, Yun, et al.
Publicado: (2024)