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Predicting the Curie temperature of magnetic materials with machine learning: Descriptor engineering, graph neural networks, and the role of curated data
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10.1016/j.commatsci.2026.114663 |
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COMPUTATIONAL MATERIALS SCIENCE
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Experimental Exchange Interaction Dataset for Magnetic Materials: Spin Waves to MC Simulations
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10.1038/s41597-025-06099-x |
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Scientific Data
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1D transition metal oxide chains as a challenging model for ab initio calculations
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10.1063/5.0283595 |
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The Journal of Chemical Physics
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Optimizing supercell structures for Heisenberg exchange interaction calculations
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10.1103/PhysRevB.111.144419 |
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PHYSICAL REVIEW B
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Evaluating SCAN and r2SCAN meta-GGA functionals for predicting transition temperatures in antiferromagnetic materials
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10.1103/PhysRevB.111.144406 |
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PHYSICAL REVIEW B
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Origin of A-type antiferromagnetism and chiral split magnons in altermagnetic a-MnTe
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10.1103/PhysRevB.111.104416 |
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PHYSICAL REVIEW B
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Strain-tunable magnetic and magnonic states in Ni-dihalide monolayers
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10.1103/PhysRevMaterials.8.114401 |
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PHYSICAL REVIEW MATERIALS
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Discovery of novel silicon allotropes with optimized band gaps to enhance solar cell efficiency through evolutionary algorithms and machine learning
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10.1016/j.commatsci.2024.113392 |
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COMPUTATIONAL MATERIALS SCIENCE
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Predicting superconducting transition temperature through advanced machine learning and innovative feature engineering
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10.1038/s41598-024-54440-y |
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Driven charge density modulation by spin density wave and their coexistence interplay in SmFeAsO: A first-principles study
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10.1016/j.physb.2023.415603 |
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Benchmarking density functional theory on the prediction of antiferromagnetic transition temperatures
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10.1103/PhysRevB.108.144413 |
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A deep investigation of NiO and MnO through the first principle calculations and Monte Carlo simulations
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10.1088/2516-1075/acbff8 |
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Novel first-principles insights into graphene fluorination
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10.1063/5.0091279 |
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ESpinS: A program for classical Monte-Carlo simulations of spin systems
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10.1016/j.commatsci.2021.110947 |
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Machine learning for compositional disorder: A comparison between different descriptors and machine learning frameworks
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10.1016/j.commatsci.2022.111284 |
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