Copertina

Beyond Diamond: Interpretable Machine Learning Reveals Design Principles for Quantum Defect Host Materials

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Mahshook, Mohammed, Banerjee, Rudra
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

Tuning Catalytic Efficiency: Thermodynamic Optimization of Zr-Doped \ce{Ti3C2} and \ce{Ti3CN} MXenes for HER Catalysis
di: Dutta, Shrestha, et al.
Pubblicazione: (2025)

Geometric Percolation Threshold Defines Half-Metallic Window in Vacancy-Doped Titanium disulfides
di: Dutta, Shrestha, et al.
Pubblicazione: (2026)

Na in Diamond: High Spin Defects Revealed by the ADAQ High-Throughput Computational Database
di: Davidsson, Joel, et al.
Pubblicazione: (2023)

Machine Learning Enables Optimization of Diamond for Quantum Applications
di: deQuilettes, Dane W., et al.
Pubblicazione: (2025)

Machine Learning and First-Principles Predictions of Materials with Low Lattice Thermal Conductivity
di: Lin, Chia-Min, et al.
Pubblicazione: (2024)

Design Principles and Identification of Birefringent Materials
di: Jung, Gwan Yeong, et al.
Pubblicazione: (2025)

Machine Learning Approaches to Point Defects in Non-Metallic Materials: A Review of Methods
di: Kumagai, Yu, et al.
Pubblicazione: (2026)

From Defects to Devices: Design Guidelines for High-Performance Diamond-Based Solar Cells and Single-Dopant Diodes
di: Kaintz, Matúš, et al.
Pubblicazione: (2026)

Interpretable Machine Learning for High-Strength High-Entropy Alloy Design
di: Bajpai, Anurag, et al.
Pubblicazione: (2024)

Beyond Predicted ZT: Machine Learning Strategies for the Experimental Discovery of Thermoelectric Materials
di: Athar, Shoeb, et al.
Pubblicazione: (2026)

Applications of Machine Learning in Polymer Materials: Property Prediction, Material Design, and Systematic Processes
di: Li, Hongtao Guo Shuai Li Shu
Pubblicazione: (2025)

Revealing Quantum Geometry in Nonlinear Quantum Materials
di: Jiang, Yiyang, et al.
Pubblicazione: (2025)

Rational Design of Efficient Defect-Based Quantum Emitters
di: Turiansky, Mark E., et al.
Pubblicazione: (2024)

Combining Machine Learning Models with First-Principles High-Throughput Calculation to Accelerate the Search of Promising Thermoelectric Materials
di: Fan, Tao, et al.
Pubblicazione: (2024)

Impact of Nitrogen Atom Clusters and Vacancy Defects on Graphene: A Molecular Dynamics Investigation
di: Rudra, Indranil, et al.
Pubblicazione: (2025)

From Mono- to Hexa-Interstitials: Computational Insights into Carbon Defects in Diamond
di: Cherati, Nima Ghafari, et al.
Pubblicazione: (2025)

Quantum-Annealing Enhanced Machine Learning for Interpretable Phase Classification of High-Entropy Alloys
di: Hoyos, Diego Ibarra, et al.
Pubblicazione: (2025)

Materials Discovery With Quantum-Enhanced Machine Learning Algorithms
di: Graña, Ignacio F., et al.
Pubblicazione: (2025)

Data-Driven Review and Machine Learning Prediction of Diamond Vacancy Center Synthesis
di: Jiang, Zhi, et al.
Pubblicazione: (2025)

Interpretable Nanoporous Materials Design with Symmetry-Aware Networks
di: Zhou, Zhenhao, et al.
Pubblicazione: (2025)

Physically Interpretable Descriptors Drive the Materials Design of Metal Hydrides for Hydrogen Storage
di: Jang, Seong-Hoon, et al.
Pubblicazione: (2025)

Composite Material Design for Optimized Fracture Toughness Using Machine Learning
di: Jahromi, Mohammad Naqizadeh, et al.
Pubblicazione: (2024)

Rotational Soft Modes and Octahedral Distortion as Design Principles for Ultralow Thermal Conductivity in Halide Materials
di: Wu, Yu, et al.
Pubblicazione: (2026)

Harnessing Machine Learning for Quantum-Accurate Predictions of Non-Equilibrium Behavior in 2D Materials
di: Zhang, Yue, et al.
Pubblicazione: (2025)

Theoretical Design of a Shallow Donor in Diamond by Lithium-Nitrogen Codoping
di: Moussa, Jonathan E., et al.
Pubblicazione: (2012)

Active-Learning Inspired $\textit{Ab Initio}$ Theory-Experiment Loop Approach for Management of Material Defects: Application to Superconducting Qubits
di: Chaudhari, Sarvesh, et al.
Pubblicazione: (2025)

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

Multi-fidelity Machine Learning Interatomic Potentials for Charged Point Defects
di: Wang, Xinwei, et al.
Pubblicazione: (2026)

2025 Quantum Diamond Workshop Findings Report
di: Braje, Danielle A., et al.
Pubblicazione: (2025)

Materials Beyond Hamiltonian Limits -- Quantum Measurement as a Resource for Material Design
di: Mannhart, Jochen
Pubblicazione: (2026)

Quantum Kernel Machine Learning for Autonomous Materials Science
di: Adams, Felix, et al.
Pubblicazione: (2026)

Machine Learning-Enhanced Design of Lead-Free Halide Perovskite Materials Using Density Functional Theory
di: Kumar, Upendra, et al.
Pubblicazione: (2024)

Evolution from Landau Quantization to Discrete Scale Invariance Revealed by Quantum Oscillations in Topological Materials
di: Yang, Jiayi, et al.
Pubblicazione: (2026)

Machine Learning on Multiple Topological Materials Datasets
di: He, Yuqing, et al.
Pubblicazione: (2025)

Predictive Design of Defect States in Hexagonal Boron Nitride for Telecommunication-Band Quantum Emission
di: Anar, Kerem, et al.
Pubblicazione: (2026)

Weyl Semimetals: from Principles, Materials to Applications
di: Zhong, Mengyuan, et al.
Pubblicazione: (2025)

Excitonic Landscapes in Monolayer Lateral Heterostructures Revealed by Unsupervised Machine Learning
di: Kaur, Maninder, et al.
Pubblicazione: (2025)

Machine Learning Prediction of Charged Defect Formation Energies from Crystal Structures
di: Kiyohara, Shin, et al.
Pubblicazione: (2025)

Design Principles for Topological Thermoelectrics
di: Skinner, Brian, et al.
Pubblicazione: (2025)

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