Explainable Machine Learning for Oxygen Diffusion in Perovskites and Pyrochlores
Fuente:
arXiv
Saved in:
| Main Authors: | Lu, Grace M., Trinkle, Dallas R. |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Thermodynamics and kinetics of lithium at the silver-lithium battery interface
by: Lu, Grace M., et al.
Published: (2026)
by: Lu, Grace M., et al.
Published: (2026)
Computing ternary liquid phase diagrams: Fe-Cu-Ni
by: Trinkle, Dallas R.
Published: (2025)
by: Trinkle, Dallas R.
Published: (2025)
Core Energies in Isolated Edge and Mixed Dislocations in BCC Fe from First-principles Energy Density Method
by: Dan, Yang, et al.
Published: (2025)
by: Dan, Yang, et al.
Published: (2025)
Quantifying the contributions to diffusion in complex materials
by: Chattopadhyay, Soham, et al.
Published: (2024)
by: Chattopadhyay, Soham, et al.
Published: (2024)
Spin-polarized Energy Density Method from Spin-Density Functional Theory
by: Dan, Yang, et al.
Published: (2026)
by: Dan, Yang, et al.
Published: (2026)
External magnetic field suppression of carbon diffusion in iron
by: Wirth, Luke J., et al.
Published: (2025)
by: Wirth, Luke J., et al.
Published: (2025)
Machine Learning Reveals Composition Dependent Thermal Stability in Halide Perovskites
by: Hering, Abigail R., et al.
Published: (2025)
by: Hering, Abigail R., et al.
Published: (2025)
Diffusion-based Generative Machine Learning Model for Predicting Crack Propagation in Aluminum Nitride at the Atomic Scale
by: Lu, Jiali, et al.
Published: (2026)
by: Lu, Jiali, et al.
Published: (2026)
Energy density and stress fields in quantum systems
by: Martin, Richard M., et al.
Published: (2025)
by: Martin, Richard M., et al.
Published: (2025)
Investigating the effects of local environment on nitrogen vacancies in high entropy metal nitrides
by: DeSilva, Charith R., et al.
Published: (2025)
by: DeSilva, Charith R., et al.
Published: (2025)
Machine Learning-Driven Crystal System Prediction for Perovskites Using Augmented X-ray Diffraction Data
by: Mathew, Ansu, et al.
Published: (2026)
by: Mathew, Ansu, et al.
Published: (2026)
A Bayesian Committee Machine Potential for Oxygen-containing Organic Compounds
by: Kim, Seungwon, et al.
Published: (2024)
by: Kim, Seungwon, et al.
Published: (2024)
Predicting Organic-Inorganic Halide Perovskite Photovoltaic Performance from Optical Properties of Constituent Films through Machine Learning
by: Zhang, Ruiqi, et al.
Published: (2024)
by: Zhang, Ruiqi, et al.
Published: (2024)
Machine Learning Co-pilot for Screening of Organic Molecular Additives for Perovskite Solar Cells
by: Pu, Yang, et al.
Published: (2024)
by: Pu, Yang, et al.
Published: (2024)
Explainable AI for Curie Temperature Prediction in Magnetic Materials
by: Ajaib, M. Adeel, et al.
Published: (2025)
by: Ajaib, M. Adeel, et al.
Published: (2025)
Efficient and Accurate Spatial Mixing of Machine Learned Interatomic Potentials for Materials Science
by: Birks, Fraser, et al.
Published: (2025)
by: Birks, Fraser, et al.
Published: (2025)
Accelerating the Training and Improving the Reliability of Machine-Learned Interatomic Potentials for Strongly Anharmonic Materials through Active Learning
by: Kang, Kisung, et al.
Published: (2024)
by: Kang, Kisung, et al.
Published: (2024)
Predicting and Accelerating Nanomaterials Synthesis Using Machine Learning Featurization
by: Price, Christopher C., et al.
Published: (2024)
by: Price, Christopher C., et al.
Published: (2024)
Industrial-scale Prediction of Cement Clinker Phases using Machine Learning
by: Fayaz, Sheikh Junaid, et al.
Published: (2024)
by: Fayaz, Sheikh Junaid, et al.
Published: (2024)
Automated Machine Learning Pipeline: Large Language Models-Assisted Automated Dataset Generation for Training Machine-Learned Interatomic Potentials
by: Lahouari, Adam, et al.
Published: (2025)
by: Lahouari, Adam, et al.
Published: (2025)
Flexible Cutoff Learning: Optimizing Machine Learning Potentials After Training
by: Oerder, Rick, et al.
Published: (2026)
by: Oerder, Rick, et al.
Published: (2026)
Evaluating Universal Machine Learning Force Fields Against Experimental Measurements
by: Mannan, Sajid, et al.
Published: (2025)
by: Mannan, Sajid, et al.
Published: (2025)
Machine Learning for Improved Density Functional Theory Thermodynamics
by: Simak, Sergei I., et al.
Published: (2025)
by: Simak, Sergei I., et al.
Published: (2025)
Acceleration of Atomistic NEGF: Algorithms, Parallelization, and Machine Learning
by: Luisier, Mathieu, et al.
Published: (2026)
by: Luisier, Mathieu, et al.
Published: (2026)
Enhancing Machine Learning Potentials through Transfer Learning across Chemical Elements
by: Röcken, Sebastien, et al.
Published: (2025)
by: Röcken, Sebastien, et al.
Published: (2025)
Thermodynamic Prediction Enabled by Automatic Dataset Building and Machine Learning
by: Liu, Juejing, et al.
Published: (2025)
by: Liu, Juejing, et al.
Published: (2025)
PGNAA Spectral Classification of Aluminium and Copper Alloys with Machine Learning
by: Folz, Henrik, et al.
Published: (2024)
by: Folz, Henrik, et al.
Published: (2024)
Boltzmann Reinforcement Learning for Noise resilience in Analog Ising Machines
by: Choudhary, Aditya, et al.
Published: (2026)
by: Choudhary, Aditya, et al.
Published: (2026)
PFT: Phonon Fine-tuning for Machine Learned Interatomic Potentials
by: Koker, Teddy, et al.
Published: (2026)
by: Koker, Teddy, et al.
Published: (2026)
Materials Learning Algorithms (MALA): Scalable Machine Learning for Electronic Structure Calculations in Large-Scale Atomistic Simulations
by: Cangi, Attila, et al.
Published: (2024)
by: Cangi, Attila, et al.
Published: (2024)
ReadMOF: Structure-Free Semantic Embeddings from Systematic MOF Nomenclature for Machine Learning
by: Zhu, Kewei, et al.
Published: (2026)
by: Zhu, Kewei, et al.
Published: (2026)
Crystal Structure Prediction by Joint Equivariant Diffusion
by: Jiao, Rui, et al.
Published: (2023)
by: Jiao, Rui, et al.
Published: (2023)
Equivariant Diffusion for Crystal Structure Prediction
by: Lin, Peijia, et al.
Published: (2025)
by: Lin, Peijia, et al.
Published: (2025)
Maximizing Efficiency of Dataset Compression for Machine Learning Potentials With Information Theory
by: Yu, Benjamin, et al.
Published: (2025)
by: Yu, Benjamin, et al.
Published: (2025)
Toward Multi-Fidelity Machine Learning Force Field for Cathode Materials
by: Dong, Guangyi, et al.
Published: (2025)
by: Dong, Guangyi, et al.
Published: (2025)
Universal Machine Learning Interatomic Potentials are Ready for Solid Ion Conductors
by: Du, Hongwei, et al.
Published: (2025)
by: Du, Hongwei, et al.
Published: (2025)
Composite Material Design for Optimized Fracture Toughness Using Machine Learning
by: Jahromi, Mohammad Naqizadeh, et al.
Published: (2024)
by: Jahromi, Mohammad Naqizadeh, et al.
Published: (2024)
Hydrogen under Pressure as a Benchmark for Machine-Learning Interatomic Potentials
by: Bischoff, Thomas, et al.
Published: (2024)
by: Bischoff, Thomas, et al.
Published: (2024)
Probing Non-Equilibrium Grain Boundary Dynamics with XPCS and Domain-Adaptive Machine Learning
by: Cheng, Mouyang, et al.
Published: (2026)
by: Cheng, Mouyang, et al.
Published: (2026)
Quotient Complex Transformer (QCformer) for Perovskite Data Analysis
by: You, Xinyu, et al.
Published: (2025)
by: You, Xinyu, et al.
Published: (2025)
Similar Items
-
Thermodynamics and kinetics of lithium at the silver-lithium battery interface
by: Lu, Grace M., et al.
Published: (2026) -
Computing ternary liquid phase diagrams: Fe-Cu-Ni
by: Trinkle, Dallas R.
Published: (2025) -
Core Energies in Isolated Edge and Mixed Dislocations in BCC Fe from First-principles Energy Density Method
by: Dan, Yang, et al.
Published: (2025) -
Quantifying the contributions to diffusion in complex materials
by: Chattopadhyay, Soham, et al.
Published: (2024) -
Spin-polarized Energy Density Method from Spin-Density Functional Theory
by: Dan, Yang, et al.
Published: (2026)