High-throughput discovery of high Curie point two-dimensional ferromagnetic materials
Source abstract
Abstract
Abstract Databases for two-dimensional materials host numerous ferromagnetic materials without the vital information of Curie temperature since its calculation involves a manually intensive complex process. In this work, we develop a fully automated, hardware-accelerated, dynamic-translation based computer code, which performs first principles-based computations followed by Heisenberg model-based Monte Carlo simulations to estimate the Curie temperature from the crystal structure. We employ this code to conduct a high-throughput scan of 786 materials from a database to discover 26 materials with a Curie point beyond 400 K. For rapid data mining, we further use these results to develop an end-to-end machine learning model with generalized chemical features through an exhaustive search of the model space as well as the hyperparameters. We discover a few more high Curie point materials from different sources using this data-driven model. Such material informatics, which agrees well with recent experiments, is expected to foster practical applications of two-dimensional magnetism.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Machine Learning in Materials Science · 2D Materials and Applications · Theoretical and Computational Physics
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (52)
Show source-provided references
1. Huang, B. et al. Layer-dependent ferromagnetism in a van der Waals crystal down to the monolayer limit. Nature 546, 270–273 (2017).10.1038/nature22391 ↗
2. Gong, C. et al. Discovery of intrinsic ferromagnetism in two-dimensional van der Waals crystals. Nature 546, 265 (2017).10.1038/nature22060 ↗
3. Feng, Y. P. et al. Prospects of spintronics based on 2D materials. Wiley Interdiscip. Rev. Comput. Mol. Sci. 7, e1313 (2017).
4. Farooq, M. U. & Hong, J. Switchable valley splitting by external electric field effect in graphene/CrI3 heterostructures. npj 2D Mater. Appl. 3, 3 (2019).10.1038/s41699-019-0086-6 ↗
5. Soumyanarayanan, A., Reyren, N., Fert, A. & Panagopoulos, C. Emergent phenomena induced by spin-orbit coupling at surfaces and interfaces. Nature 539, 509–517 (2016).10.1038/nature19820 ↗
6. Mermin, N. D. & Wagner, H. Absence of ferromagnetism or antiferromagnetism in one- or two-dimensional isotropic Heisenberg models. Phys. Rev. Lett. 17, 1133–1136 (1966).10.1103/PhysRevLett.17.1133 ↗
7. Bonilla, M. et al. Strong room-temperature ferromagnetism in VSe2 monolayers on van der Waals substrates. Nat. Nanotechnol. 13, 289–293 (2018).10.1038/s41565-018-0063-9 ↗
8. O’Hara, D. J. et al. Room temperature intrinsic ferromagnetism in epitaxial manganese selenide films in the monolayer limit. Nano Lett. 18, 3125–3131 (2018).10.1021/acs.nanolett.8b00683 ↗
9. van Gog, H. et al. Thermal stability and electronic and magnetic properties of atomically thin 2D transition metal oxides. npj 2D Mater. Appl. 3, 18 (2019).10.1038/s41699-019-0100-z ↗
10. Zhu, Y., Kong, X., Rhone, T. D. & Guo, H. Systematic search for two-dimensional ferromagnetic materials. Phys. Rev. Mater. 2, 81001 (2018).10.1103/PhysRevMaterials.2.081001 ↗
11. Kumar, H. et al. Tunable magnetism and transport properties in nitride MXenes. ACS Nano 11, 7648–7655 (2017).10.1021/acsnano.7b02578 ↗
12. Jiang, Z., Wang, P., Xing, J., Jiang, X. & Zhao, J. Screening and design of Novel 2D ferromagnetic materials with high curie temperature above room temperature. ACS Appl. Mater. Interfaces 10, 39032–39039 (2018).10.1021/acsami.8b14037 ↗
13. Zhuang, H. L., Xie, Y., Kent, P. R. C. & Ganesh, P. Computational discovery of ferromagnetic semiconducting single-layer CrSnTe3. Phys. Rev. B 92, 35407 (2015).10.1103/PhysRevB.92.035407 ↗
14. Miao, N., Xu, B., Zhu, L., Zhou, J. & Sun, Z. 2D intrinsic ferromagnets from van der Waals antiferromagnets. J. Am. Chem. Soc. 140, 2417–2420 (2018).10.1021/jacs.7b12976 ↗
15. Mounet, N. et al. Two-dimensional materials from high-throughput computational exfoliation of experimentally known compounds. Nat. Nanotechnol. 13, 246–252 (2018).10.1038/s41565-017-0035-5 ↗
16. Haastrup, S. et al. The Computational 2D Materials Database: high-throughput modeling and discovery of atomically thin crystals. 2D Mater. 5, 42002 (2018).10.1088/2053-1583/aacfc1 ↗
17. Zhou, J. et al. 2DMatPedia, an open computational database of two-dimensional materials from top-down and bottom-up approaches. Sci. Data 6, 86 (2019).
18. Horton, M. K., Montoya, J. H., Liu, M. & Persson, K. A. High-throughput prediction of the ground-state collinear magnetic order of inorganic materials using density functional theory. npj Comput. Mater. 5, 64 (2019).10.1038/s41524-019-0199-7 ↗
19. Goodenough, J. B. An interpretation of the magnetic properties of the perovskite-type mixed crystals La1−xSrxCoO3−λ. J. Phys. Chem. Solids 6, 287–297 (1958).10.1016/0022-3697(58)90107-0 ↗
20. Kanamori, J. Superexchange interaction and symmetry properties of electron orbitals. J. Phys. Chem. Solids 10, 87–98 (1959).10.1016/0022-3697(59)90061-7 ↗
21. Ong, S. P. et al. Python Materials Genomics (pymatgen): a robust, open-source python library for materials analysis. Comput. Mater. Sci. 68, 314–319 (2013).10.1016/j.commatsci.2012.10.028 ↗
22. Torelli, D. & Olsen, T. Calculating critical temperatures for ferromagnetic order in two-dimensional materials. 2D Mater. 6, 15028 (2018).10.1088/2053-1583/aaf06d ↗
23. Ashton, M. et al. Two-dimensional intrinsic half-metals with large spin gaps. Nano Lett. 17, 5251–5257 (2017).10.1021/acs.nanolett.7b01367 ↗
24. Zhang, J. et al. Janus monolayer transition-metal dichalcogenides. ACS Nano 11, 8192–8198 (2017).10.1021/acsnano.7b03186 ↗
25. Sreepal, V. et al. Two-dimensional covalent crystals by chemical conversion of thin van der Waals materials. Nano Lett. 19, 6475–6481 (2019).10.1021/acs.nanolett.9b02700 ↗
26. Fei, Z. et al. Two-dimensional itinerant ferromagnetism in atomically thin Fe3GeTe2. Nat. Mater. 17, 778–782 (2018).10.1038/s41563-018-0149-7 ↗
27. Zhuang, H. L., Kent, P. R. C. & Hennig, R. G. Strong anisotropy and magnetostriction in the two-dimensional Stoner ferromagnet Fe3GeTe2. Phys. Rev. B 93, 134407 (2016).10.1103/PhysRevB.93.134407 ↗
28. Wang, X. et al. Raman spectroscopy of atomically thin two-dimensional magnetic iron phosphorus trisulfide (FePS3). Cryst. 2D Mater. 3, 31009 (2016).10.1088/2053-1583/3/3/031009 ↗
29. Kim, K. et al. Suppression of magnetic ordering in XXZ-type antiferromagnetic monolayer NiPS3. Nat. Commun. 10, 345 (2019).
30. Cococcioni, M. & de Gironcoli, S. Linear response approach to the calculation of the effective interaction parameters in the LDA+U method. Phys. Rev. B 71, 35105 (2005).10.1103/PhysRevB.71.035105 ↗
31. Schmidt, J., Marques, M. R. G., Botti, S. & Marques, M. A. L. Recent advances and applications of machine learning in solid-state materials science. npj Comput. Mater. 5, 83 (2019).10.1038/s41524-019-0221-0 ↗
32. Nelson, J. & Sanvito, S. Predicting the Curie temperature of ferromagnets using machine learning. Phys. Rev. Mater. 3, 104405 (2019).10.1103/PhysRevMaterials.3.104405 ↗
33. Ward, L. et al. Matminer: an open source toolkit for materials data mining. Comput. Mater. Sci. 152, 60–69 (2018).10.1016/j.commatsci.2018.05.018 ↗
34. Olson, R. S., Bartley, N., Urbanowicz, R. J. & Moore, J. H. Evaluation of a tree-based pipeline optimization tool for automating data science. In Proc Genetic and Evolutionary Computation Conference 2016 (ed. Friedrich, T.) 485–492 (ACM, 2016).10.1145/2908812.2908918 ↗
35. Tao, L. et al. Silicene field-effect transistors operating at room temperature. Nat. Nanotechnol. 10, 227–231 (2015).10.1038/nnano.2014.325 ↗
36. Saha, D. & Mahapatra, S. Atomistic modeling of the metallic-to-semiconducting phase boundaries in monolayer MoS2. Appl. Phys. Lett. 108, 253106 (2016).10.1063/1.4954257 ↗
37. Kappera, R. et al. Phase-engineered low-resistance contacts for ultrathin MoS2 transistors. Nat. Mater. 13, 1128 (2014).10.1038/nmat4080 ↗
38. Zheng, F., Zhou, Z., Liu, X. & Feng, J. First-principles study of charge and magnetic ordering in monolayer NbSe2. Phys. Rev. B 97, 81101 (2018).10.1103/PhysRevB.97.081101 ↗
39. Hjorth Larsen, A. et al. The atomic simulation environment—a Python library for working with atoms. J. Phys. Condens. Matter 29, 273002 (2017).10.1088/1361-648X/aa680e ↗
40. Kresse, G. & Furthmüller, J. Efficiency of ab-initio total energy calculations for metals and semiconductors using a plane-wave basis set. Comput. Mater. Sci. 6, 15–50 (1996).10.1016/0927-0256(96)00008-0 ↗
41. Kresse, G. & Joubert, D. From ultrasoft pseudopotentials to the projector augmented-wave method. Phys. Rev. B 59, 1758–1775 (1999).10.1103/PhysRevB.59.1758 ↗
42. Perdew, J. P., Burke, K. & Ernzerhof, M. Generalized Gradient Approximation Made Simple. Phys. Rev. Lett. 77, 3865–3868 (1996).10.1103/PhysRevLett.77.3865 ↗
43. Hacene, M. et al. Accelerating VASP electronic structure calculations using graphic processing units. J. Comput. Chem. 33, 2581–2589 (2012).10.1002/jcc.23096 ↗
44. Dudarev, S. L., Botton, G. A., Savrasov, S. Y., Humphreys, C. J. & Sutton, A. P. Electron-energy-loss spectra and the structural stability of nickel oxide: An LSDA+U study. Phys. Rev. B 57, 1505–1509 (1998).10.1103/PhysRevB.57.1505 ↗
45. Hu, L. et al. Two-dimensional materials from high-throughput computational exfoliation of experimentally known compounds. Phys. Rev. B 6, 1–7 (2017).10.1103/PhysRevB.6.1 ↗
46. Momma, K. & Izumi, F. VESTA3 for three-dimensional visualization of crystal, volumetric and morphology data. J. Appl. Crystallogr 44, 1272–1276 (2011).10.1107/S0021889811038970 ↗
47. Wang, L., Maxisch, T. & Ceder, G. Oxidation energies of transition metal oxides within the GGA+U framework. Phys. Rev. B 73, 195107 (2006).10.1103/PhysRevB.73.195107 ↗
48. Vatansever, E. et al. Strain effects on electronic and magnetic properties of the monolayer α - RuCl3: A first-principles and Monte Carlo study. J. Appl. Phys. 125, 83903 (2019).10.1063/1.5078713 ↗
49. Lam, S. K., Pitrou, A. & Seibert, S. Numba: a LLVM-based Python JIT Compiler. In Proc. 2nd Workshop on the LLVM Compiler Infrastructure in HPC (ed. Finkel, H.) 7:1–7:6 (ACM, 2015).10.1145/2833157.2833162 ↗
50. Pedregosa, F. et al. Scikit-learn: Machine Learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011).
51. Nosé, S. A unified formulation of the constant temperature molecular dynamics methods. J. Chem. Phys. 81, 511–519 (1984).10.1063/1.447334 ↗
52. Hoover, W. G. Canonical dynamics: equilibrium phase-space distributions. Phys. Rev. A 31, 1695–1697 (1985).10.1103/PhysRevA.31.1695 ↗