Imagen de Portada

A Shift-Invariant Deep Learning Framework for Automated Analysis of XPS Spectra

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Saddiq, Issa, Fan, Yuxin, Palgrave, Robert G., Isaacs, Mark A., Morgan, David, Butler, Keith T.
Formato:	Preprint
Publicado:	2026
Materias:	Materials Science
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

XPS Insight Note: Coster – Kronig Broadening
por: Mark A. Isaacs, et al.
Publicado: (2025)

Entropy-based Active Learning of Graph Neural Network Surrogate Models for Materials Properties
por: Allotey, Johannes, et al.
Publicado: (2021)

XPS analysis of molecular contamination and sp2 amorphous carbon on oxidized (100) diamond
por: Vidrio, Ricardo, et al.
Publicado: (2023)

Learning disentangled latent representations facilitates discovery and design of functional materials
por: Cha, Jaehoon, et al.
Publicado: (2025)

The carbon cost of materials discovery: Can machine learning really accelerate the discovery of new photovoltaics?
por: Walker, Matthew, et al.
Publicado: (2025)

XPS Analysis of Surface Chemical Transformations in ZBLAN Glass under Thermal and Vibrational Stimuli
por: Subedi, Ayush, et al.
Publicado: (2025)

General Learning of the Electric Response of Inorganic Materials
por: Martin, Bradley A. A., et al.
Publicado: (2025)

Tracking the Lithiation State of Li$_x$Si from Machine-Learned XPS Binding Energies
por: Bertran, Michael Alejandro Hernandez, et al.
Publicado: (2026)

Leveraging transfer learning for accurate estimation of ionic migration barriers in solids
por: Devi, Reshma, et al.
Publicado: (2025)

Optimal pre-train/fine-tune strategies for accurate material property predictions
por: Devi, Reshma, et al.
Publicado: (2024)

Machine Learning Phonon Spectra for Fast and Accurate Optical Lineshapes of Defects
por: Turiansky, Mark E., et al.
Publicado: (2025)

Delta Machine Learning for Predicting Dielectric Properties and Raman Spectra
por: Grumet, Manuel, et al.
Publicado: (2023)

Crystal Structure Generation with Autoregressive Large Language Modeling
por: Antunes, Luis M., et al.
Publicado: (2023)

Operando XPS in Reactive Plasmas: The Importance of The Wall Reactions
por: Diulus, J. Trey, et al.
Publicado: (2025)

Zinc blende ZnS (001) surface structure investigated by XPS,LEED and DFT
por: Oliveira, Pablo, et al.
Publicado: (2023)

OmniXAS: A Universal Deep-Learning Framework for Materials X-ray Absorption Spectra
por: Kharel, Shubha R., et al.
Publicado: (2024)

A literature-derived dataset of migration barriers for quantifying ionic transport in battery materials
por: Devi, Reshma, et al.
Publicado: (2025)

Effect of UV light irradiation on charge neutralization in XPS measurements
por: Zhu, Lei, et al.
Publicado: (2024)

Conditional Generative Models Enable Targeted Exploration of MAX Phase Design Space
por: Swain, Jamie, et al.
Publicado: (2026)

Two-Dimensional Graphene-like BeO Sheet: A Promising Deep-Ultraviolet Nonlinear Optical Materials System with Strong and Highly Tunable Second Harmonic Generation
por: Liu, Linlin, et al.
Publicado: (2025)

Self-Optimizing Machine Learning Potential Assisted Automated Workflow for Highly Efficient Complex Systems Material Design
por: Li, Jiaxiang, et al.
Publicado: (2025)

Auger parameter analysis for TiN and AlN thin films via combined in-situ XPS and HAXPES
por: Pshyk, O. V., et al.
Publicado: (2025)

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

Atomic and electronic structure of poly-[Ni(Salen)]: combined study by XPS, UV PES, NEXAFS and DFT methods
por: Korusenko, Petr M., et al.
Publicado: (2025)

Effects of Grain Boundaries and Surfaces on Electronic and Mechanical Properties of Solid Electrolytes
por: Xie, Weihang, et al.
Publicado: (2023)

Deep Neural Network for Phonon-Assisted Optical Spectra in Semiconductors
por: Gu, Qiangqiang, et al.
Publicado: (2025)

Origin of zigzag antiferromagnetic orders in XPS3 (X= Fe, Ni) monolayers
por: Li, Ping, et al.
Publicado: (2024)

XPS Insight Note—Strategies for Obtaining Peak Shapes and Inferring Chemistry
por: D. J. Morgan, et al.
Publicado: (2025)

How Can Machine Learning Accelerate CALPHAD Free Energy Modeling?
por: Shen, Chen, et al.
Publicado: (2026)

Automated Machine Learning Pipeline: Large Language Models-Assisted Automated Dataset Generation for Training Machine-Learned Interatomic Potentials
por: Lahouari, Adam, et al.
Publicado: (2025)

Deep Learning-Based Extraction of Promising Material Groups and Common Features from High-Dimensional Data: A Case of Optical Spectra of Inorganic Crystals
por: Takahashi, Akira, et al.
Publicado: (2025)

Electron Emission Yield Datasets Under Electron Impact From Surfaces Characterized In Situ by XPS or AES
por: Belhaj, M., et al.
Publicado: (2025)

Deep Learning for Quantitative Dynamic Fragmentation Analysis
por: Cazares, Erwin, et al.
Publicado: (2024)

Hierarchical Deep Research with Local-Web RAG: Toward Automated System-Level Materials Discovery
por: Ding, Rui, et al.
Publicado: (2025)

Machine Learning Based Optical Thermometry Using Photoluminescence and Raman Spectra of Diamonds Containing SiV Centers
por: Hossain, Md Shakhawath, et al.
Publicado: (2025)

Accelerating Domain-Aware Electron Microscopy Analysis Using Deep Learning Models with Synthetic Data and Image-Wide Confidence Scoring
por: Lynch, Matthew J., et al.
Publicado: (2024)

Automated and Scalable SEM Image Analysis of Perovskite Solar Cell Materials via a Deep Segmentation Framework
por: Pan, Jian Guo, et al.
Publicado: (2025)

Learning Radical Excited States from Sparse Data
por: Shen, Jingkun, et al.
Publicado: (2024)

Deep Learning to Automate Parameter Extraction and Model Fitting of Two-Dimensional Transistors
por: Bennett, Robert K. A., et al.
Publicado: (2025)

Discovering new photovoltaics using optimal transport theory
por: Walker, Matthew A. H., et al.
Publicado: (2026)