Chemical control of polarization in thin strained films of a multiaxial ferroelectric: Phase diagrams and polarization rotation

Morozovska, Anna N.; Eliseev, Eugene A.; Biswas, Arpan; Shevliakova, Hanna V.; Morozovsky, Nicholas V.; Kalinin, Sergei V.

doi:10.1103/physrevb.105.094112

Cited by 9 publications

(1 citation statement)

References 74 publications

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“…Bayesian optimization (BO) or (multi-objective) BO, 16,[23][24][25][26][27] has been originally developed as a low computationally cost global optimization tool for design problems having expensive black-box objective functions. BO has been extensively applied for rapid exploration of large material [28][29][30][31][32][33][34][35] and chemical 36,37 control parameters and/or functional properties space exploration to enable optimization towards desired device applications. Here, the BO replicates the expensive function evaluations with a cheap (scalable) surrogate model and then utilizes an adaptive sampling technique through maximizing an acquisition function to learn or update the knowledge of the parameter space towards finding the optimal region.…”

Section: Human-in-the Loop Based Automated Experiments (Ae) Workflowmentioning

confidence: 99%

A dynamic Bayesian optimized active recommender system for curiosity-driven partially “Human-in-the-loop” automated experiments

Biswas¹,

Liu

Creange

et al. 2023

Preprint

Self Cite

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Optimization of experimental materials synthesis and characterization through active learning methods has been growing over the last decade, with examples ranging from measurements of diffraction on combinatorial alloys at synchrotrons, to searches through chemical space with automated synthesis robots for perovskites. In virtually all cases, the target property of interest for optimization is defined apriori with the ability to shift the trajectory of the optimization based on human-identified findings during the experiment is lacking. Thus, to highlight the best of both human operators and AI-driven experiments, here we present the development of a new type of human-AI collaborated experimental workflow, via a Bayesian optimized active recommender system (BOARS), to shape targets on the fly with human real-time feedback. Here, the human guidance overpowers AI at early iteration when prior knowledge (uncertainty) is minimal (higher), while the AI overpowers the human during later iterations to accelerate the process with the human assessed goal. We showcase examples of this framework applied to pre-acquired piezoresponse force spectroscopy of a ferroelectric thin film, and in real time on an atomic force microscope, with human assessment to find symmetric hysteresis loops. It is found that such features appear more affected by subsurface defects than the local domain structure. This work shows the utility of human-AI approaches for curiosity-driven exploration of systems across experimental domains.

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Section: Human-in-the Loop Based Automated Experiments (Ae) Workflowmentioning

confidence: 99%

A dynamic Bayesian optimized active recommender system for curiosity-driven partially “Human-in-the-loop” automated experiments

Biswas¹,

Liu

Creange

et al. 2023

Preprint

Self Cite

View full text Add to dashboard Cite

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An Emergent Quadruple Phase Ensemble in Doped Bismuth Ferrite Thin Films Through Site and Strain Engineering

Zhou,

Huang,

Kobayashi

et al. 2024

Adv Funct Materials

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In ferroic materials, giant susceptibilities can be realized at artificially constructed phase boundaries through deterministic manipulation of the order parameter. Here, emergent ferroelectric structural phase evolution behavior is demonstrated through a synergistic combination of A‐site doping and strain engineering. Using chemical solution deposition derived (001)‐oriented Sm‐substituted bismuth ferrite (Bi1‐xSmxFeO3) films as a prototypical system, a morphotropic phase boundary comprising a coexistence of four distinct crystallographic phases is uncovered. These ferroelectric, polar, and nonpolar phases form a nanoscale mixture without the presence of crystallographically hard boundaries. The system thus possesses the ability to show both polarization rotation and extension, effectively releasing the polarization from its crystallographic constraint. Consequently, both robust ferroelectric properties and giant electromechanical responses are obtained. For instance, the optimized composition with x = 0.14 has a remnant polarization of 2Pr = 103 µC cm−2 and electromechanical response 175% that of undoped BFO. These findings showcase the tremendous potential of synthetic phase boundaries, particularly in the context of lead‐free functional multiferroics.

show abstract

A dynamic Bayesian optimized active recommender system for curiosity-driven partially Human-in-the-loop automated experiments

Biswas,

Liu,

Creange

et al. 2024

npj Comput Mater

View full text Add to dashboard Cite

Optimization of experimental materials synthesis and characterization through active learning methods has been growing over the last decade, with examples ranging from measurements of diffraction on combinatorial alloys at synchrotrons, to searches through chemical space with automated synthesis robots for perovskites. In virtually all cases, the target property of interest for optimization is defined a priori with the ability to shift the trajectory of the optimization based on human-identified findings during the experiment is lacking. Thus, to highlight the best of both human operators and AI-driven experiments, here we present the development of a human–AI collaborated experimental workflow, via a Bayesian optimized active recommender system (BOARS), to shape targets on the fly with human real-time feedback. Here, the human guidance overpowers AI at early iteration when prior knowledge (uncertainty) is minimal (higher), while the AI overpowers the human during later iterations to accelerate the process with the human-assessed goal. We showcase examples of this framework applied to pre-acquired piezoresponse force spectroscopy of a ferroelectric thin film, and in real-time on an atomic force microscope, with human assessment to find symmetric hysteresis loops. It is found that such features appear more affected by subsurface defects than the local domain structure. This work shows the utility of human–AI approaches for curiosity driven exploration of systems across experimental domains.

show abstract

Chemical control of polarization in thin strained films of a multiaxial ferroelectric: Phase diagrams and polarization rotation

Cited by 9 publications

References 74 publications

A dynamic Bayesian optimized active recommender system for curiosity-driven partially “Human-in-the-loop” automated experiments

A dynamic Bayesian optimized active recommender system for curiosity-driven partially “Human-in-the-loop” automated experiments

An Emergent Quadruple Phase Ensemble in Doped Bismuth Ferrite Thin Films Through Site and Strain Engineering

A dynamic Bayesian optimized active recommender system for curiosity-driven partially Human-in-the-loop automated experiments

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