Data-driven discovery of novel synthesizable MAX phase materials
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: MXene and MAX Phase Materials · 2D Materials and Applications · Ferroelectric and Negative Capacitance Devices
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (100+)
Show source-provided references
1. The MN+1AXN phases: a new class of solids10.1016/S0079-6786(00)00006-6 ↗
2. On the chemical diversity of the MAX phases10.1016/j.trechm.2019.02.016 ↗
3. Die Kristallstruktur von Ti3SiC2—Ein neuer komplexcarbid-typ10.1007/BF00899949 ↗
4. Synthesis and characterization of a remarkable ceramic: ti3SiC210.1111/j.1151-2916.1996.tb08018.x ↗
5. Layered machinable ceramics for high temperature applications10.1016/S1359-6462(96)00418-6 ↗
6. Microstructure of Ti3SiC2-based ceramics10.1016/0167-577X(95)00259-6 ↗
7. Die kristallstruktur von Ti3GeC2, monatshefte für chem10.1007/BF00902438 ↗
8. Summary of constitutional data on the aluminum-carbon-titanium system10.1007/BF02647559 ↗
9. High-resolution transmission electron microscopy of Ti4AlN3, or Ti3Al2N2 revisited10.1111/j.1151-2916.1999.tb02117.x ↗
10. Trends in electronic structures and structural properties of MAX phases: a first-principles study on M2AlC (M = Sc, Ti, Cr, Zr, Nb, Mo, Hf, or Ta), M2AlN, and hypothetical M2AlB phases10.1088/0953-8984/26/50/505503 ↗
11. Hybrid exchange-correlation functional study of the structural, electronic, and mechanical properties of the MAX phases10.1063/1.3536513 ↗
12. Theoretical stability and materials synthesis of a chemically ordered MAX phase, Mo2ScAlC2, and its two-dimensional derivate Mo2ScC2 MXene10.1016/j.actamat.2016.12.008 ↗
13. Phase stability of nanolaminated epitaxial (Cr1–xFex)2AlC MAX Phase thin films on MgO(111) and Al2O3(0001) for use as conductive coatings10.1021/acsanm.1c03166 ↗
14. Prediction of MAX phases, VN+1SiCN (N=1,2), from first-principles theory10.1063/1.2405721 ↗
15. High-throughput study and machine learning on MAX and MAB phases: new materials and fingerprints of superior lattice thermal conductivities10.1016/j.actamat.2023.119001 ↗
16. Elastic and mechanical properties of the MAX phases10.1146/annurev-matsci-062910-100448 ↗
17. Physical properties investigations of ternary-layered carbides M2PbC (M = Ti, Zr and Hf): first-principles calculations10.3390/cryst11121445 ↗
18. 7 - Electrical properties of MAX phases
19. Recent progress in additive manufacturing of bulk MAX phase components: a review10.1016/j.jmst.2022.05.026 ↗
20. Electronic structures of iMAX phases and their two-dimensional derivatives: a family of piezoelectric materials
21. Titanium germanium carbide MAX phase electrocatalysts for supercapacitors and alkaline water electrolysis processes
22. Rational synthesis of mixed metal oxide clusters supported on a partially etched MAX phase for efficient electrocatalytic CO2 conversion
23. Effect of Ti3AlC2 MAX phase on electrochemical performance of thermo-responsive copolymer electrolyte for solid state zinc-ion battery
24. A new high-temperature durable absorber material solution through a spinel-type high solar absorptivity coating on Ti2AlC MAX phase material10.1021/acsami.1c10585 ↗
25. Double transition metal MXene (TixTa4−xC3) 2D materials as anodes for Li-ion batteries10.1038/s41598-020-79991-8 ↗
26. Demonstration of Li-ion capacity of MAX phases10.1021/acsenergylett.6b00488 ↗
27. Structure and synthesis of MAX phase materials: a brief review10.1080/10408436.2021.1966384 ↗
28. Synthesis of (Ti1−xWx)3SiC2 MAX phase solid solution and its high-temperature oxidation performance10.1007/s10854-022-08564-4 ↗
29. Two-dimensional nanocrystals produced by exfoliation of Ti3AlC210.1002/adma.201102306 ↗
30. Two-dimensional transition metal carbides10.1021/nn204153h ↗
31. Synthesis of Mo4VAlC4 MAX phase and two-dimensional Mo4VC4 MXene with five atomic layers of transition metals10.1021/acsnano.9b07708 ↗
32. Two-dimensional, ordered, double transition metals carbides (MXenes)10.1021/acsnano.5b03591 ↗
33. New two-dimensional niobium and vanadium carbides as promising materials for Li-ion batteries10.1021/ja405735d ↗
34. 2D MXenes for electromagnetic shielding: a review
35. MXenes for electromagnetic interference shielding: experimental and theoretical perspectives
36. Mo2TiAlC2: a new ordered layered ternary carbide10.1016/j.scriptamat.2014.12.024 ↗
37. Experimental and theoretical characterization of ordered MAX phases Mo2TiAlC2 and Mo2Ti2AlC310.1063/1.4929640 ↗
38. In- and out-of-plane ordered MAX phases and their MXene derivatives, in: 2D met10.1007/978-3-030-19026-2_3 ↗
39. Novel MAB phases and insights into their exfoliation into 2D MBenes10.1039/C9NR01267B ↗
40. Computational prediction of boron-based MAX phases and MXene derivatives10.1021/acs.chemmater.0c02139 ↗
41. MAX and MAB phases: two-dimensional layered carbide and boride nanomaterials for electrochemical applications10.1021/acsanm.9b01526 ↗
42. High-entropy laminate metal carbide (MAX Phase) and its two-dimensional derivative MXene10.1021/acs.chemmater.1c03348 ↗
43. Superlattice MAX phases with A-layers reconstructed into 0D-clusters, 1D-chains, and 2D-lattices10.1021/acs.jpcc.3c02233 ↗
44. Beyond metals: theoretical discovery of semiconducting MAX phases and their potential application in thermoelectrics10.1039/D4CP01950D ↗
45. The rise of MAX phase alloys – large-scale theoretical screening for the prediction of chemical order and disorder10.1039/D2NR02414D ↗
46. Ab initio predictions of structure and physical properties of the Zr2GaC and Hf2GaC MAX phases under pressure10.1038/s41598-021-82402-1 ↗
47. Theoretical prediction, synthesis, and crystal structure determination of new MAX phase compound V2SnC10.1007/s40145-020-0391-8 ↗
48. Prediction and synthesis of a family of atomic laminate phases with Kagomé-like and in-plane chemical ordering10.1126/sciadv.1700642 ↗
49. MAX phases – Past, present, and future
50. Stability trends of $MAX$ phases from first principles10.1103/PhysRevB.81.220102 ↗
51. First-principles determination of multicomponent hydride phase diagrams: application to the Li-Mg-N-H system10.1002/adma.200700843 ↗
52. High-throughput computational discovery of ternary-layered MAX phases and prediction of their exfoliation for formation of 2D MXenes10.1039/D0NR08791B ↗
53. Predicting the electronic and structural properties of two-dimensional materials using machine learning
54. The high-throughput highway to computational materials design10.1038/nmat3568 ↗
55. Machine-learning-assisted accurate band gap predictions of functionalized MXene10.1021/acs.chemmater.8b00686 ↗
56. Machine learning for alloys10.1038/s41578-021-00340-w ↗
57. Remarkable optoelectronic characteristics of synthesizable square-octagon haeckelite structures: machine learning materials discovery
58. Accelerating the discovery of transition metal borides by machine learning on small data sets10.1021/acsami.3c03657 ↗
59. Materials Data toward machine learning: advances and challenges10.1021/acs.jpclett.2c00576 ↗
60. LAX phases: a family of novel stable layered materials, informatics-based discovery10.1002/inf2.12664 ↗
61. Maximizing the mechanical performance of Ti3AlC2-based MAX phases with aid of machine learning10.1007/s40145-022-0612-4 ↗
62. Machine learning for high performance organic solar cells: current scenario and future prospects10.1039/D0EE02838J ↗
63. Machine learning: an advanced platform for materials development and state prediction in lithium-ion batteries10.1002/adma.202101474 ↗
64. Machine-learning-assisted materials discovery using failed experiments10.1038/nature17439 ↗
65. Machine-learning interatomic potentials enable first-principles multiscale modeling of lattice thermal conductivity in graphene/borophene heterostructures10.1039/D0MH00787K ↗
66. Instability and spontaneous reconstruction of few-monolayer thick GaN graphitic structures10.1021/acs.nanolett.6b01225 ↗
67. Accelerating materials property predictions using machine learning10.1038/srep02810 ↗
68. Rajan · Mater. Inform. Mater. Today · 2005 · 8 · 3810.1016/S1369-7021(05)71123-8 ↗
69. Prediction of thermoelectric performance for layered IV-V-VI semiconductors by high-throughput ab initio calculations and machine learning10.1038/s41524-021-00645-y ↗
70. Prediction of synthesis of 2D metal carbides and nitrides (MXenes) and their precursors with positive and unlabeled machine learning10.1021/acsnano.8b08014 ↗
71. Highly interpretable machine learning framework for prediction of mechanical properties of nickel based superalloys
72. Chemical hardness-driven interpretable machine learning approach for rapid search of photocatalysts10.1038/s41524-021-00669-4 ↗
73. Accelerated prediction of Vickers hardness of Co- and Ni-based superalloys from microstructure and composition using advanced image processing techniques and machine learning10.1016/j.actamat.2020.06.042 ↗
74. Coupling the high-throughput property map to machine learning for predicting lattice thermal conductivity10.1021/acs.chemmater.9b01046 ↗
75. A series of MAX phases with MA-triangular-prism bilayers and elastic properties10.1002/anie.201814128 ↗
76. Generalized gradient approximation made simple10.1103/PhysRevLett.77.3865 ↗
77. From ultrasoft pseudopotentials to the projector augmented-wave method10.1103/PhysRevB.59.1758 ↗
78. Efficient iterative schemes for ab initio total-energy calculations using a plane-wave basis set10.1103/PhysRevB.54.11169 ↗
79. Special points for Brillouin-zone integrations10.1103/PhysRevB.13.5188 ↗
80. First-principles phonon calculations with phonopy and Phono3py10.7566/JPSJ.92.012001 ↗
81. Implementation strategies in phonopy and phono3py10.1088/1361-648X/acd831 ↗
82. USPEX—Evolutionary crystal structure prediction10.1016/j.cpc.2006.07.020 ↗
83. Evolutionary method for predicting surface reconstructions with variable stoichiometry10.1103/PhysRevB.87.195317 ↗
84. Pressure-induced stabilization and insulator-superconductor transition of BH10.1103/PhysRevLett.110.165504 ↗
85. First-principles determination of the structure of magnesium borohydride10.1103/PhysRevLett.109.245503 ↗
86. Novel two-dimensional boron oxynitride predicted using the USPEX evolutionary algorithm10.1039/D1CP03754D ↗
87. Treating different bonding situations: revisiting Au-Cu alloys using the random phase approximation10.1103/PhysRevB.100.045135 ↗
88. On the chemical diversity of the MAX phases10.1016/j.trechm.2019.02.016 ↗
89. Structure maps for MAX phases formability revisited
90. Zr2SeB and Hf2SeB: two new MAB phase compounds with the Cr2AlC-type MAX phase (211 phase) crystal structures10.1007/s40145-022-0646-7 ↗
91. The MAX phase borides Zr2SB and Hf2SB10.1016/j.solidstatesciences.2020.106316 ↗
92. A genomic approach to the stability, elastic, and electronic properties of the MAX phases10.1002/pssb.201451226 ↗
93. Insights into exfoliation possibility of MAX phases to MXenes10.1039/C7CP08645H ↗
94. Performance of neural networks in materials science10.1179/174328408X311053 ↗
95. Neural Networks in materials science10.2355/isijinternational.39.966 ↗
96. Mizutani · 2010
97. New tolerance factor to predict the stability of perovskite oxides and halides10.1126/sciadv.aav0693 ↗
98. The role of Hume-Rothery’s rules play in the MAX phases formability10.1016/j.mtla.2020.100810 ↗
99. Visualizing high-dimensional data using t-SNE
100. High-throughput design of $211\ensuremath{-}{\mathrm{M}}2\mathrm{AX}$ compounds