Accelerated search for BaTiO3-based piezoelectrics with vertical morphotropic phase boundary using Bayesian learning

An outstanding challenge in the nascent field of materials informatics is to incorporate materials knowledge in a robust Bayesian approach to guide the discovery of new materials. Utilizing inputs from known phase diagrams, features or material descriptors that are known to affect the ferroelectric response, and Landau–Devonshire theory, we demonstrate our approach for BaTiO(3)-based piezoelectrics with the desired target of a vertical morphotropic phase boundary. We predict, synthesize, and characterize a solid solution, (Ba(0.5)Ca(0.5))TiO(3)-Ba(Ti(0.7)Zr(0.3))O(3), with piezoelectric properties that show better temperature reliability than other BaTiO(3)-based piezoelectrics in our initial training data

A comparison between tetragonal-rhombohedral and tetragonal-orthorhombic phase boundaries on piezoelectricity enhancement

We made a detailed comparison of physical properties between the tetragonal-rhombohedral (T-R) phase boundary (in the Ba(SnTi)O3-x(BaCa)TiO3 system) and the tetragonal-orthorhombic (T-O) phase boundary (in the Ba(SnxTi1−x)O3 system). The contrasting results suggest that the T-R phase boundary enhances the piezoelectricity more significantly than the T-O phase boundary. Such difference is considered to stem from the dissimilar anisotropy for polarization rotation, the different elastic softening and the different domain wall contribution between T-R and T-O phase boundaries

Machine learning assisted design of high entropy alloys with desired property

We formulate a materials design strategy combining a machine learning (ML) surrogate model with experimental design algorithms to search for high entropy alloys (HEAs) with large hardness in a model Al-Co-Cr-Cu-Fe-Ni system. We fabricated several alloys with hardness 10% higher than the best value in the original training dataset via only seven experiments. We find that a strategy using both the compositions and descriptors based on a knowledge of the properties of HEAs, outperforms that merely based on the compositions alone. This strategy offers a recipe to rapidly optimize multi-component systems, such as bulk metallic glasses and superalloys, towards desired properties. (C) 2019 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved