Swain, MV Shape memory behavior in partially stabilized zirconia ceramics. Nature 322234–236 (1986).
Lai, A., Du, Z., Gan, CL & Schuh, CA Shape memory and small-scale superelastic ceramics. Science 3411505-1508 (2013).
Zeng, X., Du, Z., Schuh, CA & Gan, CL Enhanced shape memory and superelasticity in small volume ceramics: a perspective on control factors. MS Common. seven747–754 (2017).
Du, Z., Zhou, X., Ye, P., Zeng, X. & Gan, CL Shape-memory actuation in aligned zirconia nanofibers for artificial muscle applications at elevated temperatures. ACS Appl. Nano-matter. 32156-2166 (2020).
Lai, A. & Schuh, CA Direct electric field-induced phase transformation in paraelectric zirconia via electrical susceptibility shift. Phys. Rev. Lett. 12615701 (2021).
Pang, EL, Olson, GB & Schuh, CA The mechanism of thermal transformation hysteresis in ZrO2-CEO2 shape memory ceramic. Acta Mater. 213116972 (2021).
Gu, H. et al. Explosive and weeping ceramics. Nature 599416–420 (2021).
Jetter, J. et al. Adjustment of crystallographic compatibility to improve shape memory in ceramics. Phys. Rev. Mater. 3093603 (2019).
Song, Y., Chen, X., Dabade, V., Shield, TW & James, RD Enhanced reversibility and unusual microstructure of a phase-transformed material. Nature 50285–88 (2013).
Cui, J. et al. Combinatorial search for thermoelastic shape memory alloys with extremely low hysteresis width. Nat. Mater. 5286–290 (2006).
Zarnetta, R. et al. Identification of quaternary shape memory alloys with near-zero thermal hysteresis and unprecedented functional stability. Adv. Function Mater. 201917-1923 (2010).
Pang, EL, Olson, GB & Schuh, CA Role of grain stress on martensitic transformation in cerium oxide doped zirconia. Jam. Ceram. Soc. 1041156-1168 (2020).
Christian, JW, Olson, GB & Cohen, M. Classification of displacement transformations: what is a martensitic transformation? J.Phys. IV 5C8-3–C8-10 (1995).
Krauss, G. Martensite in steel: strength and structure. Mater. Science. Eng. A 273–27540–57 (1999).
Bhattacharya, K. Microstructure of martensite: why it forms and how it gives rise to the shape memory effect (Oxford Univ. Press, 2003).
Kelly, PM & Francis Rose, LR Martensitic transformation in ceramics – its role in transformation hardening. Program. Master Sci. 47463–557 (2002).
Wechsler, WS, Lieberman, DS & Read, TA On the theory of martensite formation. Trans. LOVE 1971503-1515 (1953).
Bowles, JS & Mackenzie, JK The crystallography of martensitic transformations I. Acta Metall. 2129-137 (1954).
Mackenzie, JK & Bowles, JS The crystallography of martensitic transformations II. Acta Metall. 2138-147 (1954).
Bowles, JS & Mackenzie, JK The crystallography of martensitic transformations III. Body-centered cubic to body-centered tetragonal transformations. Acta Metall. 2224-234 (1954).
Ball, JM & James, RD Mixtures of fine phases as energy minimizers. Camber. Ration. Mech. Anal. 10013-52 (1987).
Chen, X., Srivastava, V., Dabade, V. & James, RD Study of cofactor conditions: supercompatibility conditions between phases. J.Mech. Phys. Solids 612566-2587 (2013).
Zhang, Z., James, RD, and Müller, S. Energy barriers and hysteresis in martensitic phase transformations. Acta Mater. 574332–4352 (2009).
Delville, R. et al. Study by transmission electron microscopy of phase compatibility in shape memory alloys with low hysteresis. Philos. Mag. 90177-195 (2010).
Meng, XL, Li, H., Cai, W., Hao, SJ & Cui, LS Mechanism of thermal cycle stability of Ti50.5Neither33.5Cu11.5pd4.5 shape memory alloy with near zero hysteresis. Scr. Mater. 10330–33 (2015).
Pop-Ghe, P., Stock, N. & Quandt, E. Suppression of abnormal grain growth in K0.5N / A0.5NbO3: phase transitions and compatibility. Science. representing 919775 (2019).
Liang, YG et al. Adjustment of the hysteresis of a metal-insulator transition via network compatibility. Nat. Common. 113539 (2020).
Wegner, M., Gu, H., James, RD, and Quandt, E. Correlation between phase compatibility and efficient energy conversion in Zr-doped barium titanate. Science. representing ten3496 (2020).
Gopakumar, AM, Balachandran, PV, Xue, D., Gubernatis, JE & Lookman, T. Multi-objective optimization for materials discovery via adaptive design. Science. representing 83738 (2018).
Chen, Y. et al. Machine Learning-Assisted Multi-Objective Optimization for Material Processing Parameters: A Case Study in Mg Alloy. J. Compd Alloys. 844156159 (2020).
Kriven, WM, Fraser, WL & Kennedy, SW in Zirconia science and technology, ceramic progress Flight. 3 (eds. Heuer, AH & Hobbs, LW) 82–97 (American Ceramic Society, 1981).
Lukas, HL, Fries, SG & Sundman, B. Computational thermodynamics: the Calphad method (Cambridge Univ. Press, 2007).
Saenko, I., Ilatovskaia, M., Savinykh, G. & Fabrichnaya, O. Experimental investigation of phase relations and thermodynamic properties in ZrO2–TiO2 system. Jam. Ceram. Soc. 101386–399 (2018).
Wang, C., Zinkevich, M. & Aldinger, F. The zirconia-hafnia system: DTA measurements and thermodynamic calculations. Jam. Ceram. Soc. 893751–3758 (2006).
Park, J. et al. Thermodynamic balance of ZrO2-TiO2 system. Korean J. Ceram. seven11–15 (2001).
Xue, D. et al. Accelerated research of materials with targeted properties by adaptive design. Nat. Common. seven11241 (2016).
Trehern, W., Ortiz-Ayala, R., Atli, KC, Arroyave, R. & Karaman, I. Data-Driven Shape Memory Alloy Discovery Using the Intelligence Materials Selection Framework artificial intelligence (AIMS). Acta Mater. 228117751 (2022).
Pang, EL, McCandler, CA & Schuh, CA Reducing cracking in polycrystalline ZrO2-CEO2 shape memory ceramics by fulfilling the conditions of the cofactor. Acta Mater. 177230-239 (2019).
Gu, H., Bumke, L., Chluba, C., Quandt, E. & James, RD Phase engineering and supercompatibility of shape memory alloys. Mater. Today 21265-277 (2018).
Chluba, C. et al. Ultra low fatigue shape memory alloy films. Science 3481004-1007 (2015).
Bannister, MJ & Barnes, JM Solubility of TiO2 in ZrO2. Jam. Ceram. Soc. 69C269–C271 (1986).
Evirgen, A. et al. Relationship between crystallographic compatibility and thermal hysteresis in high temperature NiTiHf and NiTiZr Ni-rich shape memory alloys. Acta Mater. 121374-383 (2016).
Miyazaki, S. in Shape memory alloys (eds Fremond, M. & Miyazaki, S.) 69–147 (Springer, 1996).
Kainuma, R., Takahashi, S. & Ishida, K. Ductile shape memory alloys of the Cu-Al-Mn system. J.Phys. IV 5961–966 (1995).
Maki, T. Microstructure and Mechanical Behavior of Ferrous Martensite. Mater. Science. Forum 56–58157-168 (1990).
Balachandran, PV, Broderick, SR & Rajan, K. Identifying the “inorganic gene” for high-temperature piezoelectric perovskites through statistical learning. proc. R. Soc. A 4672271-2290 (2011).
Ramprasad, R., Batra, R., Pilania, G., Mannodi-Kanakkithodi, A. & Kim, C. Machine Learning in Materials Computation: Recent Applications and Prospects. npj Comput. Mater. 354 (2017).
Xue, D. et al. A computational approach to the transformation temperatures of NiTi-based shape memory alloys. Acta Mater. 125532-541 (2017).
Meredig, B. & Wolverton, C. Periodic Table Dissolution in Cubic Zirconia: Data Mining to Uncover Chemical Trends. Chem. Mater. 261985–1991 (2014).
Xue, D. et al. Accelerated search for BaTiO3piezoelectric based vertical morphotropes using Bayesian learning. proc. Natl Acad. Science. UNITED STATES 11313301–13306 (2016).
Yuan, R. et al. Accelerated discovery of large electrostrains in BaTiO3piezoelectric based on active learning. Adv. Mater. 301702884 (2018).