Copertina

How Realistic are Idealized Copper Surfaces? A Machine Learning Study of Rough Copper-Water Interfaces

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Erhard, Linus C., Schörghuber, Johannes, Comas-Vives, Aleix, Madsen, Georg K. H.
Natura:	Preprint
Pubblicazione:	2025
Soggetti:	Materials Science
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Dynamical Disorder in the Mesophase Ferroelectric HdabcoClO4: A Machine-Learned Force Field Study
di: Sødahl, Elin Dypvik, et al.
Pubblicazione: (2024)

Revealing the Atomic-Scale Structure of the Copper Sulfuric Acid Interface
di: Bhaskar, Lalith Kumar, et al.
Pubblicazione: (2026)

Machine Learning Study of Surface Reconstructions of the Cu$_{2}$O(111) Surface
di: Wadhwa, Payal, et al.
Pubblicazione: (2025)

Colorimetry and Tribology of Ultrapure Copper Surface Micromodification
di: Szczupak, Aleksandra, et al.
Pubblicazione: (2025)

PGNAA Spectral Classification of Aluminium and Copper Alloys with Machine Learning
di: Folz, Henrik, et al.
Pubblicazione: (2024)

Single Atom Substituents in Copper Surfaces May Adsorb Multiple CO Molecules
di: Christiansen, Magnus A. H., et al.
Pubblicazione: (2024)

Effect of Particle Size and Alloying with Gallium and Zinc in Copper Nanoparticles from Ab Initio Molecular Dynamics
di: Andreas Müller, et al.
Pubblicazione: (2025)

Prediction of Yield Surface of Single Crystal Copper from Discrete Dislocation Dynamics and Geometric Learning
di: Jian, Wu-Rong, et al.
Pubblicazione: (2023)

Copper alloys deterioration due to anthropogenic action
di: Duran, A., et al.
Pubblicazione: (2024)

Neural-network-enabled molecular dynamics study of HfO$_2$ phase transitions
di: Bichelmaier, Sebastian, et al.
Pubblicazione: (2024)

Active learning potentials for first-principles phase diagrams using replica-exchange nested sampling
di: Unglert, Nico, et al.
Pubblicazione: (2025)

Multiple Adsorption of CO Molecules on Transition Metal Substitutional Impurities in Copper Surfaces
di: Christiansen, Magnus A. H., et al.
Pubblicazione: (2025)

Controllable Freezing Transparency for Water Ice on Scalable Graphene Films on Copper
di: Fickl, Bernhard, et al.
Pubblicazione: (2024)

Machine-learning interatomic potentials from a users perspective: A comparison of accuracy, speed and data efficiency
di: Leimeroth, Niklas, et al.
Pubblicazione: (2025)

Electronic Signature of Melting Onset in Polycrystalline Copper at Extreme Conditions
di: Toro, Edna R., et al.
Pubblicazione: (2026)

CuXASNet: Rapid and Accurate Prediction of Copper L-edge X-Ray Absorption Spectra Using Machine Learning
di: Gleason, Samuel P., et al.
Pubblicazione: (2024)

Graphene Growth on Copper Substrate by LAMMPS Simulation
di: Hong, Lizhe
Pubblicazione: (2025)

Efficient Picosecond-Laser Lift-Off of Copper Oxide from Copper: Modelling and Experiment
di: Žemaitis, Andrius, et al.
Pubblicazione: (2026)

Accelerating first-principles molecular-dynamics thermal conductivity calculations for complex systems
di: Wieser, Sandro, et al.
Pubblicazione: (2025)

Ab-initio heat transport in defect-laden quasi-1D systems from a symmetry-adapted perspective
di: Cen, Yu-Jie, et al.
Pubblicazione: (2025)

Investigation into the Mitigation of Crevice Corrosion of Aluminum–Copper Contacts in Seawater by Regulating the Contact Surface Roughness
di: Jin Chen, et al.
Pubblicazione: (2024)

Epitaxial Stabilization and Emergent Charge Order in Copper Selenide Thin Films
di: Sharif, B. M., et al.
Pubblicazione: (2025)

Giant Negative Linear Compressibility in Orthorhombic Copper Cyanide
di: Kesari, Swayam, et al.
Pubblicazione: (2024)

Electronic Structure and Vibrational Stability of Copper-substituted Lead Apatite (LK-99)
di: Cabezas-Escares, J., et al.
Pubblicazione: (2023)

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

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

A neural-network-backed effective harmonic potential study of the ambient pressure phases of hafnia
di: Bichelmaier, Sebastian, et al.
Pubblicazione: (2024)

Structural Chart of Copper-Silver Nanoalloys through machine learning
di: Settem, Manoj, et al.
Pubblicazione: (2026)

Suppression of Thin-Film Thermal Conductivity due to Surface Roughness
di: Morita, Michimasa, et al.
Pubblicazione: (2025)

Machine Learning and Data-Driven Methods in Computational Surface and Interface Science
di: Hörmann, Lukas, et al.
Pubblicazione: (2025)

Wettability and Color Change of Copper by Controlling Area Fraction of Laser Ablated Surface
di: Gaidys, Mantas, et al.
Pubblicazione: (2024)

Tailoring the Glucose Oxidation Activity of Anodized Copper Films on Microfabricated Platforms
di: Franceschini, Filippo, et al.
Pubblicazione: (2025)

Amorphous Boron Nitride as an Ultrathin Copper Diffusion Barrier for Advanced Interconnects
di: Kaya, Onurcan, et al.
Pubblicazione: (2024)

Polarity-dependent Electroadhesion at Silicon Interfaces with Nanoscale Roughness
di: Peng, Liang, et al.
Pubblicazione: (2025)

Operando Plasma-XPS for Process Monitoring: Hydrogenation of Copper Oxide Confined Under h-BN Case Study
di: Diulus, J. Trey, et al.
Pubblicazione: (2024)

Copper Vapor Catalyzing Role in the Growth of Graphene
di: Chang, Yuan, et al.
Pubblicazione: (2025)

Operando Characterization and Molecular Simulations Reveal the Growth Kinetics of Graphene on Liquid Copper during Chemical Vapor Deposition
di: Rein, Valentina, et al.
Pubblicazione: (2023)

Copper Stress Response of Antibiotic‐Resistant and Copper‐Tolerant Enterococcus faecium and the Antibacterial Efficacy of Functionalized Copper Surfaces
di: Franca Arndt, et al.
Pubblicazione: (2025)

Quantitative predictions of the thermal conductivity in transition metal dichalcogenides: The impact of point defects in MoS$_2$ and WS$_2$ monolayers
di: Mahendran, Srinivisan, et al.
Pubblicazione: (2023)

Modelling atomic and nanoscale structure in the silicon-oxygen system through active machine learning
di: Erhard, Linus C., et al.
Pubblicazione: (2023)