Composition Design of Shape Memory Ceramics based on Gaussian Processes
Authors:
Ashutosh Pandey,
Justin Jetter,
Hanlin Gu,
Eckhard Quandt,
Richard D. James
Abstract:
We present a Gaussian process machine learning model to predict the transformation temperature and lattice parameters of ZrO$_2$-based ceramics. Our overall goal is to search for a shape memory ceramic with a reversible transformation and low hysteresis. The identification of a new low hysteresis composition is based on design criteria that have been successful in metal alloys: (1) $λ_2 = 1$, wher…
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We present a Gaussian process machine learning model to predict the transformation temperature and lattice parameters of ZrO$_2$-based ceramics. Our overall goal is to search for a shape memory ceramic with a reversible transformation and low hysteresis. The identification of a new low hysteresis composition is based on design criteria that have been successful in metal alloys: (1) $λ_2 = 1$, where $λ_2$ is the middle eigenvalue of the transformation stretch tensor, (2) minimizing the max$|q(f)|$, which measures the deviation from satisfying the cofactor conditions, (3) high transformation temperature, (4) low transformational volume change, and (5) solid solubility. We generate many synthetic compositions, and identify a promising composition, 31.75Zr-37.75Hf-14.5Y-14.5Ta-1.5Er, which closely satisfies all the design criteria based on predictions from machine learning. However, differential thermal analysis reveals a relatively high thermal hysteresis of 137°C for this composition, indicating that the proposed design criteria are not universally applicable to all ZrO$_2$-based ceramics. We also explore reducing tetragonality of the austenite phase by addition of Er$_2$O$_3$. The idea is to tune the lattice parameters of austenite phase towards a cubic structure will increase the number of martensite variants, thus, allowing more flexibility for them to accommodate high strain during transformation. We find the effect of Er$_2$O$_3$ on tetragonality is weak due to limited solubility. We conclude that a more effective dopant is needed to achieve significant tetragonality reduction. Overall, Gaussian process machine learning models are shown to be highly useful for prediction of compositions and lattice parameters, but the discovery of low hysteresis ceramic materials apparently involves other factors not relevant to phase transformations in metals.
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Submitted 4 April, 2026; v1 submitted 2 April, 2025;
originally announced April 2025.
In-situ compression and shape recovery of Ceramic single grain micro-pillar
Authors:
Justin Jetter,
Eckhard Quandt
Abstract:
Most ceramic materials are known for high fracture toughness while reacting highly brittle to physical deformation. Some advancements were made by utilizing the transformation toughening effect of Yttria-doped Zirconia. However, finding a ceramic material demonstrating an effect analogous to the Shape Memory Effect (SME) in certain metals, that also allows for superelastic responses, remains a cha…
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Most ceramic materials are known for high fracture toughness while reacting highly brittle to physical deformation. Some advancements were made by utilizing the transformation toughening effect of Yttria-doped Zirconia. However, finding a ceramic material demonstrating an effect analogous to the Shape Memory Effect (SME) in certain metals, that also allows for superelastic responses, remains a challenge. The underlying mechanism for SME and superelasticity is based on crystallographic variations within the material's grains, requiring sophisticated electron microscopy techniques for direct observation. The combination of a scanning electron microscope (SEM) with focused ion beam (FIB) milling, a Kleindiek Nanotechnik GmbH micro-manipulator with a 1.5 $μ$m diamond tip, and the ability to achieve in-situ heating up to 450 °C on a Kleindiek heating stage provides a robust platform for the preparation, deformation, and heating of micro-pillars made from ceramic materials. This setup enabled us to conduct detailed studies on the Zirconia-based ceramic, observing permanent deformation exceeding 4% strain, followed by shape recovery at 370 °C. The paper provides outlines the key experimental steps that facilitated these observations.
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Submitted 2 April, 2025;
originally announced April 2025.