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Scanning Probe Microscopy of Combinatorial Libraries

Authors:Yu Liu, Aditya Raghavan, Utkarsh Bratush, Maxim Zayatdinov, Chih-Yu Li, Rohit Pant, Ichiro Takeuchi, Bushun Hsieh, Albert Soceva, Edgar Dimitrov, Mauricio Terrones, Venkatraman Gopalan, Ian Mercer, R. Jackson Sperling, John Paul Maria, Sergey V. Kalinin

View PDF of the article “An automated materials discovery platform: microscopic scanning of combinatorial libraries,” by Yu Liu and 15 other authors

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a summary:Libraries of harmonic materials provide a powerful platform for mapping how physical properties evolve across binary and ternary cross sections of multicomponent phase diagrams. While the synthesis of such libraries has advanced since the 1960s and has been accelerated by laboratory automation, their broader utility depends on rapid quantitative measurements of composition-dependent structures and functions. Scanning probe microscopy (SPM), including piezo response force microscopy (PFM), offers unique capabilities to provide these functionally relevant, spatially resolved readouts. Here we present a fully automated SPM framework for exploring ferroelectric properties via conformational libraries, with a focus on Sm-doped BiFeO3 (SmBFO) binary systems and Al$_{1-xy}$Sc$_x$B$_y$N (Al,Sc,B)N binary systems. In SmBFO, automated exploration identifies known morphotropic phase boundaries with enhanced photoelectric response and reveals a previously unreported double-peak fine structure. In the (Al,Sc,B)N library, ferroelectric behavior appears at the phase stability boundary, and is related to changes in morphology and defect concentration. By integrating automated SPM with wavelength dispersive spectroscopy (WDS) and photoluminescence mapping, we resolve the composition, morphology, and defect relationships underlying the photoelectric response and show a path toward a multi-instrument, high-throughput characterization platform. Finally, we implement single- and multi-objective Bayesian optimization based on Gaussian processes to enable autonomous exploration, highlighting the Pareto front as a powerful framework for balancing competing material rewards and accelerating data-driven physics discovery.

Submission date

From: Yu Liu [view email]
[v1]

Tue, 24 Dec 2024 00:39:51 UTC (1,900 KB)
[v2]

Monday, 17 November 2025, 20:04:38 UTC (8,883 KB)

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2025-11-20 05:00:00

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