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Low-hysteresis shape-memory ceramics designed by multimode modelling

Abstract

Zirconia ceramics exhibit a martensitic phase transformation that enables large strains of order 10%, making them prospects for shape-memory and superelastic applications at high temperature1,2,3,4,5. Similarly to other martensitic materials, this transformation strain can be engineered by carefully alloying to produce a more commensurate transformation with reduced hysteresis (difference in transformation temperature on heating and cooling)6,7,8,9,10,11. However, such ‘lattice engineering’ in zirconia is complicated by additional physical constraints: there is a secondary need to manage a large transformation volume change12, and to achieve transformation temperatures high enough to avoid kinetic barriers6. Here we present a method of augmenting the lattice engineering approach to martensite design to address these additional constraints, incorporating modern computational thermodynamics and data science tools to span complex multicomponent spaces for which no data yet exist. The result is a new zirconia composition with record low hysteresis of 15 K, which is about ten times less transformation hysteresis compared to typical values (and approximately five times less than the best values reported so far). This finding demonstrates that zirconia ceramics can exhibit hysteresis values of the order of those of widely deployed shape-memory alloys, paving the way for their use as viable high-temperature shape-memory materials.

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Fig. 1: Multifaceted modelling approach combining machine learning, computational thermodynamics and lattice engineering to predict shape-memory characteristics of new ZrO2-based compositions.
Fig. 2: Effect of various dopants on Ms, volume change (ΔV/V) and λ2 in the binary systems for ZrO2 shape-memory ceramics with correspondence C.
Fig. 3: Characterization of preferred compositions in the ZrO2-TiO2-AlO1.5 system.
Fig. 4: Low thermal hysteresis in the present ZrO2-based shape-memory ceramics.

Data availability

The training dataset for the ML models used in this work is available at https://doi.org/10.5281/zenodo.5895143. Additional data related to this work are available from the authors upon request.

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Acknowledgements

The authors thank S. Patala and C. Houser for useful discussions, as well as D. Schwalbe-Koda, J. Paras and H. Oh for preliminary calculations, which helped guide this work. This work was sponsored by the US Army Research Office under grant W911NF-21-2-0159, and in part by the US Army Research Office through the Institute for Soldier Nanotechnologies, under cooperative agreement number W911NF-18-2-0048. This work made use of the MRSEC Shared Experimental Facilities at MIT, supported by the NSF under award number DMR-1419807. E.L.P. acknowledges support from the NSF Graduate Research Fellowship Program under grant number DGE-1745302.

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E.L.P, C.A.S. and G.B.O developed the research theme and contributed to the design of the workflow. E.L.P. wrote all code, performed all calculations and conducted all experiments. E.L.P, C.A.S and G.B.O. analysed the data, discussed the results, made joint decisions about directions pursued and contributed to writing and reviewing the manuscript.

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Correspondence to Christopher A. Schuh.

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Pang, E.L., Olson, G.B. & Schuh, C.A. Low-hysteresis shape-memory ceramics designed by multimode modelling. Nature 610, 491–495 (2022). https://doi.org/10.1038/s41586-022-05210-1

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