Articles

type: Journal
Title DOI Date Conference / journal
Predicting the Curie temperature of magnetic materials with machine learning: Descriptor engineering, graph neural networks, and the role of curated data 10.1016/j.commatsci.2026.114663 COMPUTATIONAL MATERIALS SCIENCE
Experimental Exchange Interaction Dataset for Magnetic Materials: Spin Waves to MC Simulations 10.1038/s41597-025-06099-x Scientific Data
1D transition metal oxide chains as a challenging model for ab initio calculations 10.1063/5.0283595 The Journal of Chemical Physics
Evaluating SCAN and r2SCAN meta-GGA functionals for predicting transition temperatures in antiferromagnetic materials 10.1103/PhysRevB.111.144406 PHYSICAL REVIEW B
Optimizing supercell structures for Heisenberg exchange interaction calculations 10.1103/PhysRevB.111.144419 PHYSICAL REVIEW B
Origin of A-type antiferromagnetism and chiral split magnons in altermagnetic a-MnTe 10.1103/PhysRevB.111.104416 PHYSICAL REVIEW B
Strain-tunable magnetic and magnonic states in Ni-dihalide monolayers 10.1103/PhysRevMaterials.8.114401 PHYSICAL REVIEW MATERIALS
Discovery of novel silicon allotropes with optimized band gaps to enhance solar cell efficiency through evolutionary algorithms and machine learning 10.1016/j.commatsci.2024.113392 COMPUTATIONAL MATERIALS SCIENCE
Driven charge density modulation by spin density wave and their coexistence interplay in SmFeAsO: A first-principles study 10.1016/j.physb.2023.415603
Predicting superconducting transition temperature through advanced machine learning and innovative feature engineering 10.1038/s41598-024-54440-y
Benchmarking density functional theory on the prediction of antiferromagnetic transition temperatures 10.1103/PhysRevB.108.144413
A deep investigation of NiO and MnO through the first principle calculations and Monte Carlo simulations 10.1088/2516-1075/acbff8
Novel first-principles insights into graphene fluorination 10.1063/5.0091279
Machine learning for compositional disorder: A comparison between different descriptors and machine learning frameworks 10.1016/j.commatsci.2022.111284
ESpinS: A program for classical Monte-Carlo simulations of spin systems 10.1016/j.commatsci.2021.110947
type: Conference
Title Date
پيش بيني دماي نيل مواد پادفرومغناطيس با استفاده از يادگيري ماشين
تاثير فضاي ويژگي و روش مناسب در پيش بيني دماي گذار مواد ابررسانا با استفاده از روش هاي يادگيري ماشين
اثر تعداد پيكربندي مغناطيسي در محاسبه برهمكنش هاي هايزنبرگ در رهيافت نظريه تابعي چگالي
سنچش روش DFT+U در پيش بيني دماي بحراني
بررسي ويژگيهاي مختلف براي پيش بيني دماي كوري با استفاده از يادگيري ماشين