Imagen de Portada

Symmetry- and Gradient-enhanced Gaussian Process Regression for the Active Learning of Potential Energy Surfaces in Porous Materials

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Krondorfer, Johannes K., Binder, Christian W., Hauser, Andreas W.
Formato:	Preprint
Publicado:	2025
Materias:	Chemical Physics Materials Science Computational Physics
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

The Ridge Integration Method and its Application to Molecular Sieving, Demonstrated for Gas Purification via Graphdiyne Membranes
por: Binder, Christian W., et al.
Publicado: (2025)

Magnetic coupling between nuclear motion and nuclear spins in molecules
por: Diez, Matthias, et al.
Publicado: (2026)

Molecular Pseudorotation in Phthalocyanines as a Tool for Magnetic Field Control at the Nanoscale
por: Wilhelmer, Raphael, et al.
Publicado: (2025)

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

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

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

Learning Potential Energy Surfaces of Hydrogen Atom Transfer Reactions in Peptides
por: Neubert, Marlen, et al.
Publicado: (2025)

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

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

Enhanced Performance of Highly Activated Carbon and Surface-Treated Porous Polymers as Physical Adsorbents for Chemical Warfare Agents
por: Park, Sanghyeon, et al.
Publicado: (2025)

Surface Properties of Ga-Cu Based Liquid-Metal Alloys: Impact of Cu Dilution Topography and Reaction Conditions
por: Hsieh, Tzung-En, et al.
Publicado: (2025)

Learning Ordering in Crystalline Materials with Symmetry-Aware Graph Neural Networks
por: Peng, Jiayu, et al.
Publicado: (2024)

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

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

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

Active Learning for Generalizable Detonation Performance Prediction of Energetic Materials
por: Ullberg, R. Seaton, et al.
Publicado: (2026)

Epitaxial hexagonal boron nitride for hydrogen generation by radiolysis of interfacial water
por: Binder, Johannes, et al.
Publicado: (2022)

Impact of Subsurface Oxygen on CO2 Charging Energy Changes in Cu Surfaces
por: Lei, Xiaohe, et al.
Publicado: (2024)

Translational eigenstates of He@C$_{60}$ from four-dimensional \textit{ab initio} Potential Energy Surfaces interpolated using Gaussian Process Regression
por: Panchagnula, K., et al.
Publicado: (2024)

Ab Initio Free Energy Surfaces for Coupled Ion-Electron Transfer
por: Abraham, Ethan, et al.
Publicado: (2025)

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

Cyclic Voltammetry of Ion-Coupled Electron Transfer Reactions for Diagnosing Energy Storage Materials
por: Malaie, Keyvan
Publicado: (2025)

Exploring Quantum Active Learning for Materials Design and Discovery
por: Lourenço, Maicon Pierre, et al.
Publicado: (2024)

Potential Energy Curves of Hydrogenic Halides HX(F,Cl,Br) and i-DMFT Method
por: Pilon, H Olivares, et al.
Publicado: (2026)

ChemNLP: A Natural Language Processing based Library for Materials Chemistry Text Data
por: Choudhary, Kamal, et al.
Publicado: (2022)

Adapting OC20-trained EquiformerV2 Models for High-Entropy Materials
por: Clausen, Christian M., et al.
Publicado: (2024)

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

Discovery of 2D Materials via Symmetry-Constrained Diffusion Model
por: Xu, Shihang, et al.
Publicado: (2024)

From Evaluation to Design: Using Potential Energy Surface Smoothness Metrics to Guide Machine Learning Interatomic Potential Architectures
por: Liu, Ryan, et al.
Publicado: (2026)

Cavity-Born Oppenheimer Approximation for Molecules and Materials via Electric Field Response
por: Bonini, John, et al.
Publicado: (2024)

Porous carbon nitride fullerenes: a novel family of porous cage molecules
por: Fthenakis, Zacharias G., et al.
Publicado: (2024)

Integrating Graph Neural Networks and Many-Body Expansion Theory for Potential Energy Surfaces
por: Chen, Siqi, et al.
Publicado: (2024)

Demonstrating Electrochemical CO$_2$ Capture on Redox-Active Metal-Organic Frameworks
por: Vetik, Iuliia, et al.
Publicado: (2024)

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

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

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

Efficient Long-Range Machine Learning Force Fields for Liquid and Materials Properties
por: Weber, John L., et al.
Publicado: (2025)

Spherical vs. Non-Spherical and Symmetry-Preserving vs. Symmetry-Breaking Densities of Open-Shell Atoms in Density Functional Theory
por: Chowdhury, Shah Tanvir ur Rahman, et al.
Publicado: (2021)

Magnetically Assisted Separation of Weakly Magnetic Metal Ions in Porous Media.Part 1: Experiments
por: Nwachukwu, Alwell, et al.
Publicado: (2025)

Gauge-Invariant Long-Wavelength TDDFT Without Empty States: From Polarizability to Kubo Conductivity Across Heterogeneous Materials
por: Tantardini, Christian, et al.
Publicado: (2025)