Geometric pattern and chemical environment in high magnetization materials: A data-driven study
Bibliographic record
Abstract
No abstract is available for this record. The assistant can identify its topic from the title, but cannot infer the study’s results.
Source: Not provided · Quality: missing
Research context
OpenAlex topics: Magnetic Properties of Alloys · Machine Learning in Materials Science · Magnetic Properties and Applications
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (45)
Show source-provided references
1. Cullity · 2011
2. Permanent magnet applications10.1016/S0304-8853(02)00335-9 ↗
3. Pr-Fe and Nd-Fe-based materials: A new class of high-performance permanent magnets (invited)10.1063/1.333571 ↗
4. Processing of permanent magnet materials based on rare earth–transition metal intermetallics10.1016/0966-9795(95)97285-9 ↗
5. On-the-fly machine-learning for high-throughput experiments: search for rare-earth-free permanent magnets10.1038/srep06367 ↗
6. Bifunctional CoFe2O4/ZnO core/shell nanoparticles for magnetic fluid hyperthermia with controlled optical response10.1021/acs.jpcc.7b11115 ↗
7. Magnetic MXene: A machine-learning model with small data10.1109/TMAG.2023.3287988 ↗
8. Electronic structure calculations with dynamical mean-field theory10.1103/RevModPhys.78.865 ↗
9. Physical theory of ferromagnetic domains10.1103/RevModPhys.21.541 ↗
10. Inhomogeneous electron gas10.1103/PhysRev.136.B864 ↗
11. Self-consistent equations including exchange and correlation effects10.1103/PhysRev.140.A1133 ↗
12. Predicting lattice thermal conductivity of semiconductors from atomic-information-enhanced CGCNN combined with transfer learning
13. Prediction of large magnetic moment materials with graph neural networks and random forests
14. Compositional descriptor-based recommender system for the materials discovery10.1063/1.5016210 ↗
15. Atomic property weighted radial distribution functions descriptors of metal–organic frameworks for the prediction of gas uptake capacity10.1021/jp404287t ↗
16. Fast and accurate modeling of molecular atomization energies with machine learning10.1103/PhysRevLett.108.058301 ↗
17. Hund’s matrix: A structure-based descriptor to predict the magnetic properties10.1016/j.jmmm.2024.172026 ↗
18. Describing atomic environments with smooth overlap of atomic positions
19. Accelerating the discovery of novel magnetic materials using machine learning-guided adaptive feedback10.1073/pnas.2204485119 ↗
20. Developing an improved crystal graph convolutional neural network framework for accelerated materials discovery
21. A simple feature enriched CGCNN for predicting magnetization in transition metal compounds10.1016/j.jmmm.2025.173541 ↗
22. Convolutional neural networks for Crystal Material property prediction using hybrid Orbital-Field matrix and Magpie descriptors10.3390/cryst9040191 ↗
23. Crystal structure representations for machine learning models of formation energies10.1002/qua.24917 ↗
24. Computational design of magnetic molecules and their environment using quantum chemistry, machine learning and multiscale simulations10.1038/s41570-022-00424-3 ↗
25. Nearest neighbour analysis of random distributions on a sphere10.1093/mnras/241.2.109 ↗
26. An accelerating approach of designing ferromagnetic materials via machine learning modeling of magnetic ground state and Curie temperature10.1080/21663831.2020.1863876 ↗
27. Advancing magnetic material discovery through machine learning: Unveiling new manganese-based materials
28. Voronoi cell analysis: The shapes of particle systems10.1119/5.0087591 ↗
29. Machine learning reveals orbital interaction in crystalline materials10.1080/14686996.2017.1378060 ↗
30. Orbital Field Matrix (OFM): A physics-informed approach to encoding orbital interactions and electronic environments at the atomic level
31. Crystal graph convolutional neural networks for an accurate and interpretable prediction of material properties10.1103/PhysRevLett.120.145301 ↗
32. Arti Kashyap; Density functional theory and material databases in the era of machine learning
33. Coey · 2010
34. Goodenough · 1963
35. A mathematical theory of communication10.1002/j.1538-7305.1948.tb01338.x ↗
36. A unified approach to interpreting model predictions
37. T. Akiba, S. Sano, T. Yanase, T. Ohta, M. Koyama, Optuna: A next-generation hyperparameter optimization framework, in: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2019, pp. 2623–2631.10.1145/3292500.3330701 ↗
38. LightGBM: A highly efficient gradient boosting decision tree
39. From local explanations to global understanding with explainable AI for trees10.1038/s42256-019-0138-9 ↗
40. On the interpretation of x2 from contingency tables, and the calculation of P10.2307/2340521 ↗
41. Controlling the false discovery rate: A practical and powerful approach to multiple testing10.1111/j.2517-6161.1995.tb02031.x ↗
42. The control of the false discovery rate in multiple testing under dependency10.1214/aos/1013699998 ↗
43. Use of ranks in one-criterion variance analysis10.1080/01621459.1952.10483441 ↗
44. Python materials genomics (pymatgen): A robust, open-source python library for materials analysis10.1016/j.commatsci.2012.10.028 ↗
45. Spglib: a software library for crystal symmetry search