Image de couverture de livre

Machine learning approach for vibronically renormalized electronic band structures

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Aryal, Niraj, Zhang, Sheng, Yin, Weiguo, Chern, Gia-Wei
Format:	Preprint
Publié:	2024
Sujets:	Materials Science Machine Learning
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Machine learning for structure-property relationships: Scalability and limitations
par: Tian, Zhongzheng, et autres
Publié: (2023)

Kinetics of orbital ordering in cooperative Jahn-Teller models: Machine-learning enabled large-scale simulations
par: Ghosh, Supriyo, et autres
Publié: (2024)

Coarsening of chiral domains in itinerant electron magnets: A machine learning force field approach
par: Fan, Yunhao, et autres
Publié: (2024)

On the origin of metal-insulator transitions in the parent compounds of ruthenium-pnictide superconductors
par: Aryal, Niraj, et autres
Publié: (2024)

Machine learning nonequilibrium phase transitions in charge-density wave insulators
par: Fan, Yunhao, et autres
Publié: (2026)

Machine learning for structure-guided materials and process design
par: Morand, Lukas, et autres
Publié: (2023)

Machine-learning force-field models for dynamical simulations of metallic magnets
par: Chern, Gia-Wei, et autres
Publié: (2026)

Machine-learning modeling of magnetization dynamics in quasi-equilibrium and driven metallic spin systems
par: Chern, Gia-Wei, et autres
Publié: (2026)

Enhanced coarsening of charge density waves induced by electron correlation: Machine-learning enabled large-scale dynamical simulations
par: Yang, Yang, et autres
Publié: (2024)

Advancing Magnetic Materials Discovery -- A structure-based machine learning approach for magnetic ordering and magnetic moment prediction
par: Verma, Apoorv, et autres
Publié: (2025)

Accurate predictive model of band gap with selected important features based on explainable machine learning
par: Lee, Joohwi, et autres
Publié: (2025)

Machine-learned models for magnetic materials
par: Leszczyński, Paweł, et autres
Publié: (2023)

Machine learning enhanced atom probe tomography analysis: a snapshot review
par: Li, Yue, et autres
Publié: (2025)

AlphaCrystal-II: Distance matrix based crystal structure prediction using deep learning
par: Song, Yuqi, et autres
Publié: (2024)

Accelerating the discovery of low-energy structure configurations: a computational approach that integrates first-principles calculations, Monte Carlo sampling, and Machine Learning
par: Musa, Md Rajib Khan, et autres
Publié: (2024)

Quantum-Annealing Enhanced Machine Learning for Interpretable Phase Classification of High-Entropy Alloys
par: Hoyos, Diego Ibarra, et autres
Publié: (2025)

Machine learning force-field model for kinetic Monte Carlo simulations of itinerant Ising magnets
par: Tyberg, Alexa, et autres
Publié: (2024)

Machine learning driven search of hydrogen storage materials
par: Banerjee, Tanumoy, et autres
Publié: (2025)

Unsupervised learning for structure detection in plastically deformed crystals
par: Barbot, Armand, et autres
Publié: (2022)

Machine Learning Force-Field Approach for Itinerant Electron Magnets
par: Zhang, Sheng, et autres
Publié: (2025)

Machine learning assisted screening of metal binary alloys for anode materials
par: Shi, Xingyue, et autres
Publié: (2024)

Machine learning model for predicting surface wettability in laser-textured metal alloys
par: Sanandaji, Mohammad Mohammadzadeh, et autres
Publié: (2026)

Constructing accurate machine-learned potentials and performing highly efficient atomistic simulations to predict structural and thermal properties
par: Liu, Junlan, et autres
Publié: (2024)

Accelerating superconductor discovery through tempered deep learning of the electron-phonon spectral function
par: Gibson, Jason B., et autres
Publié: (2024)

Machine-learning-enabled interpretation of tribological deformation patterns in large-scale MD data
par: Ehrich, Hendrik J., et autres
Publié: (2025)

Global Universal Scaling and Ultra-Small Parameterization in Machine Learning Interatomic Potentials with Super-Linearity
par: Hu, Yanxiao, et autres
Publié: (2025)

Learning from the electronic structure of molecules across the periodic table
par: Kaniselvan, Manasa, et autres
Publié: (2025)

Statistical learning of structure-property relationships for transport in porous media, using hybrid AI modeling
par: Hosseinhashemi, Somayeh, et autres
Publié: (2025)

Machine learning-guided construction of an analytic kinetic energy functional for orbital free density functional theory
par: Manzhos, Sergei, et autres
Publié: (2025)

Machine learning frontier orbital energies of nanodiamonds
par: Kirschbaum, Thorren, et autres
Publié: (2022)

Model Accuracy and Data Heterogeneity Shape Uncertainty Quantification in Machine Learning Interatomic Potentials
par: Shuang, Fei, et autres
Publié: (2025)

Machine learning Hubbard parameters with equivariant neural networks
par: Uhrin, Martin, et autres
Publié: (2024)

A physics-aware deep learning model for shear band formation around collapsing pores in shocked reactive materials
par: Cheng, Xinlun, et autres
Publié: (2025)

Predicting band gap from chemical composition: A simple learned model for a material property with atypical statistics
par: Ma, Andrew, et autres
Publié: (2025)

Assessing data-driven predictions of band gap and electrical conductivity for transparent conducting materials
par: Ottomano, Federico, et autres
Publié: (2024)

Advancing atomic electron tomography with neural networks
par: Lee, Juhyeok, et autres
Publié: (2025)

A deep learning approach to search for superconductors from electronic bands
par: Li, Jun, et autres
Publié: (2024)

Ferromagnetic ordering in mazelike stripe liquid of a dipolar six-state clock model
par: Simmons, Quentin, et autres
Publié: (2024)

Shotgun crystal structure prediction using machine-learned formation energies
par: Liu, Chang, et autres
Publié: (2023)

Large non-saturating Nernst thermopower and magnetoresistance in compensated semimetal ScSb
par: Laha, Antu, et autres
Publié: (2025)