Abstract
Combinatorial spread libraries offer an approach to explore the evolution of material properties over broad concentration, temperature, and growth parameter spaces. However, the traditional limitation of this approach is the requirement for the read-out of functional properties across the library. Here we develop automated piezoresponse force microscopy (PFM) for the exploration of combinatorial spread libraries and demonstrate its application in the SmxBi1-xFeO3 system with the ferroelectric-antiferroelectric morphotropic phase boundary. This approach relies on the synergy of the quantitative nature of PFM and the implementation of automated experiments that allow PFM-based sampling of macroscopic samples. The concentration dependence of pertinent ferroelectric parameters was determined and used to develop the mathematical framework based on the Ginzburg-Landau theory describing the evolution of these properties across the concentration space. We pose that a combination of automated scanning probe microscope and combinatorial spread library approach will emerge as an efficient research paradigm to close the characterization gap in high-throughput materials discovery. We make the data sets open to the community, and we hope that this will stimulate other efforts to interpret and understand the physics of these systems.
Original language | English |
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Journal | ACS Nano |
DOIs | |
State | Accepted/In press - 2024 |
Funding
The PFM characterization and analysis (Y.L.) were conducted at the Center for Nanophase Materials Sciences (CNMS), which is a US Department of Energy, Office of Science User Facility, at Oak Ridge National Laboratory. This effort was supported (S.V.K. and A.R., data analysis and acquisitions) by the Center for 3D Ferroelectric Microelectronics (3DFeM), an Energy Frontier Research Center funded by the U.S. Department of Energy (DOE), Office of Science, Basic Energy Sciences, under Award DE-SC0021118. E.A.E. and A.N.M. acknowledge (theory development) the DOE Software Project on \u201CComputational Mesoscale Science and Open Software for Quantum Materials\u201D under Award DE-SC0020145 as part of the Computational Materials Sciences Program of the US Department of Energy, Office of Science, Basic Energy Sciences. The work at the University of Maryland was supported by ONR MURI N00014172661, NIST cooperative agreement 70NANB17H301, and DTRA CB11400 MAGNETO, Univ. of Maryland. The development of GPax Python package (M.Z.) was supported by the Laboratory Directed Research and Development Program at Pacific Northwest National Laboratory, a multiprogram national laboratory operated by Battelle for the U.S. Department of Energy.
Keywords
- automated microscopy
- combinatorial library
- ferroelectric
- high-throughput experimentation
- machine learning
- piezoresponse force microscopy