A study led by researchers at the Department of Energy’s Oak Ridge National Laboratory (ORNL) examines the causes behind the ordering, or pattern formation, of ions that carry a positive charge, also called cations, in double perovskite oxides, a type of metal considered promising as a potential source of cleaner, more sustainable energy for its magnetism and ability to conduct electricity.
Ayana Ghosh, an ORNL research scientist and the study’s lead author and researchers from the SRM Institute of Science and Technology in Chennai, India, sought to determine how the patterns formed by cations affect stability in double perovskites. The more stable the material, the better suited it is for potential energy applications.
If we can understand the fundamental mechanism behind these properties, then we could attempt to grow or otherwise create these perovskite materials for such applications as batteries, memory devices and capacitors. We’ve developed a formula from this study that we’ll give to the rest of the world.
This cation ordering can be affected by a number of variables. Sizes of the ions matter. Distortions come into play. We wanted to know: Is there one single factor that would drive cation ordering in this kind of system? If so, what is that factor?—Ayana Ghosh
A paper on their work was published in the journal Chemistry of Materials.
The team relied on resources from two DOE Office of Science user facilities—data collected at ORNL’s Center for Nanophase Materials Sciences and time on the Cori supercomputer at Lawrence Berkeley National Laboratory’s National Energy Research Scientific Computing Center—to develop a new computational framework.
The system combined causal analysis and traditional machine learning with density functional theory, which estimates materials’ electronic and atomic structures. Researchers trained the algorithm on various cation types and patterns within double perovskite systems to predict conditions that lead to specific ordering of cations.
The team combined the cause-and-effect relationships observed with the findings from standard predictive machine learning algorithms. Analysis of the results identified trilinear coupling, an interaction among three types of particles, as the necessary condition behind clear layered ordering—one of the essential patterns of cation ordering.
Trilinear coupling combines three types of structural modes that push the cations through the necessary phases to result in properties such as multiferroicity, the combination of magnetization and polarization that makes perovskites promising for energy applications.
If you have this type of coupling, you should have the formation of clear layered ordering. The ordering won’t occur without it. You can think of the three modes as fundamental building blocks. This wasn’t known before.—Ayana Ghosh
Next steps include applying those findings to identify conditions necessary for other types of ordering to design new phases of double perovskite oxides.
Support for this work came from the DOE Office of Science’s Office of Basic Energy Sciences. The CNMS and NERSC are DOE Office of Science user facilities.
Ayana Ghosh, Gayathri Palanichamy, Dennis P. Trujillo, Monirul Shaikh, and Saurabh Ghosh (2022) “Insights into Cation Ordering of Double Perovskite Oxides from Machine Learning and Causal Relations” Chemistry of Materials 34 (16), 7563-7578 doi: 10.1021/acs.chemmater.2c00217