High Throughput and Multimodal Materials Identification

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Multimodal studies of complex oxide compositions, featuring combinatorial synthesis, spatially-resolved characterization, data analytics, and first principles calculations.

Scientific Achievement

CFN researchers and collaborators employed artificial intelligence (AI) and machine learning (ML) enhanced data analytics and theory, along with high-throughput synthesis and spatially resolved characterization, to identify the phase composition of complex metal-oxide thin films.

Significance and Impact

This robust approach using multimodal studies and data analytics for high-throughput materials discovery may find application in areas such as microelectronics, sensors, and catalysis.

Research Details

Transition metal oxides have broad application in microelectronics, superconducting circuits, sensors, catalysis, optics, and more. However, synthesizing and characterizing functional complex metal oxides poses great challenges due to the varying compositions determined by growth and process conditions. In this study, scientists present a cross-disciplinary approach to the phase composition in a combinatorial complex metal oxide thin film, featuring high-throughput synthesis, spatially resolved characterization, artificial intelligence and machine learning enhanced data analytics, and first principles calculations.

First, the Zn-Ti-O thin film was synthesized using high throughput deposition, which contains multiple phases not known a priori. The group used X-ray absorption near edge structure (XANES), X-ray fluorescence (XRF), X-ray diffraction (XRD), and Ellipsometry to characterize the film. By analyzing the evolution of the spectroscopic signatures, coupled with multivariate curve resolution (MCR), the researchers found that the thin film contained phases of ZnO, Zn2TiO4, ZnTiO3, Zn2Ti3O8, and TiO2, each with a mixture of structure motifs. The experimental derived results were confirmed by first principles calculations.

Overall, this case study demonstrates a robust approach to multimodal studies in high-throughput materials discoveries, creating many opportunities to address future materials needs.

  • Combinatorial synthesis of complex transition metal oxide thin films (Zn-Ti-O) was done by pulsed laser deposition.
  • The spectroscopic data was analyzed using a multivariate curve resolution (MCR-ALS) algorithm and compared with first principles calculations.
  • The CFN Materials Synthesis and Theory & Computation Facilities, along with the NSLS-II Inner Shell Spectroscopy (ISS) and Beamline for Materials Measurement(BMM) beamlines were used for this study.

CFN Capabilities: X-ray absorption spectroscopy, X-ray diffraction, and ellipsometry were used to study the film with mixed phases.

Publication Reference

Li, R., Jiang, X., Zhou, C., Topsakal, M., Nykypanchuk, D., Attenkofer, K., Stacchiola, D.J., Hybertsen, M.S., Stavitski, E., Qu, X., Lu, D., and Liu, M. “Deciphering phase evolution in complex metal oxide thin films via high-throughput materials synthesis and characterization.” Nanotechnology 34, 125701 (2023).

DOI: 10.1088/1361-6528/acad09 https://iopscience.iop.org/article/10.1088/1361-6528/acad09/meta

OSTI: www.osti.gov/biblio/1960265

Acknowledgment of Support

This research was carried out at Brookhaven National Laboratory (BNL) under Contract No. DE-SC0012704 where facilities used included those of the Center for Functional Nanomaterials (CFN) and 8-ID ISS (Inner Shell Spectroscopy) and 6-BM (BMM) beamlines in the National Synchrotron Light Source II, U.S. Department of Energy Office of Science User Facilities. This work used the computational resource of the Scientific Data and Computing Center, a component of the Computational Science Initiative at BNL. Part of this research is supported by the U.S. Department of Energy, Office of Science, Office Basic Energy Sciences, under Award Number FWP PS-030. R. S. Li is supported by BNL LDRD Project No. 19-008. Helpful discussion with Dr. B. Ravel is greatly acknowledged.

2023-21264  |  INT/EXT  |  Newsroom