期刊
INORGANIC CHEMISTRY
卷 60, 期 12, 页码 9224-9232出版社
AMER CHEMICAL SOC
DOI: 10.1021/acs.inorgchem.1c01366
关键词
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资金
- National Science Foundation (NSF) under DMREF Award [1729489, DMR-1729303]
- NSF Quantum Foundry at UC Santa Barbara, through Enabling Quantum Leap: Convergent Accelerated Discovery Foundries [DMR-1906325]
- Materials Research Science and Engineering Center at UC Santa Barbara (MRSEC NSF) [DMR-1720256]
- NSF
- NSF MRSEC [DMR-1720256]
- NSF [CNS-1725797]
- Quest high performance computing facility at Northwestern University
- Office of Science of the U.S. Department of Energy [DE-AC02-05CH11231]
- Direct For Mathematical & Physical Scien
- Division Of Materials Research [1729489] Funding Source: National Science Foundation
The properties of crystalline materials are strongly correlated with their structures, and predicting crystal structure from composition requires consideration of various parameters such as atomic or ionic radii, ionicity, electronegativity, periodic table position, and magnetism. Through machine learning methods and density functional theory calculations, researchers successfully predicted 16 new candidate trirutile oxides.
The properties of crystalline materials tend to be strongly correlated with their structures, and the prediction of crystal structure from only the composition is a coveted goal in the field of inorganic materials. However, even for the simplest compositions, such prediction relies on a complex network of interactions, including atomic or ionic radii, ionicity, electronegativity, position in the periodic table, and magnetism, to name only a few important parameters. We focus here on the AB(2)X(6) (AB(2)O(6) and AB(2)F(6)) composition space with the specific goal of finding new oxide compounds in the trirutile family, which is known for unusual one-dimensional (1D) antiferromagnetic behavior. Through machine learning methods, we develop an understanding of how geometric and bonding constraints determine the crystallization of compounds in the trirutile structure as opposed to other ternary structures in this space. In combination with density functional theory (DFT) calculations, we predict 16 previously unreported candidate trirutile oxides. We successfully prepare one of these and show it forms in the disordered rutile structure, under the preparation conditions adopted here.
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