One of the main challenges in materials discovery is efficiently exploring the vast search space for targeted properties as approaches that rely on trial-and-error are impractical. We review how methods from the information sciences enable us to accelerate the search and discovery of new materials. In particular, active learning allows us to effectively navigate the search space iteratively to identify promising candidates for guiding experiments and computations. The approach relies on the use of uncertainties and making predictions from a surrogate model together with a utility function that prioritizes the decision making process on unexplored data. We discuss several utility functions and demonstrate their use in materials science applications, impacting both experimental and computational research. We summarize by indicating generalizations to multiple properties and multifidelity data, and identify challenges, future directions and opportunities in the emerging field of materials informatics.

Finding new materials with targeted properties has traditionally been guided by intuition, and trial and error. With increasing chemical complexity, the combinatorial possibilities are too large for an Edisonian approach to be practical. Here we show how an adaptive design strategy, tightly coupled with experiments, can accelerate the discovery process by sequentially identifying the next experiments or calculations, to effectively navigate the complex search space. Our strategy uses inference and global optimization to balance the trade-off between exploitation and exploration of the search space. We demonstrate this by finding very low thermal hysteresis (ΔT) NiTi-based shape memory alloys, with Ti_50.0Ni_46.7Cu_0.8Fe_2.3Pd_0.2possessing the smallest ΔT(1.84 K). We synthesize and characterize 36 predicted compositions (9 feedback loops) from a potential space of ∼800,000 compositions. Of these, 14 had smaller ΔTthan any of the 22 in the original data set.

Due to issues with Pb toxicity, there is an urgent need for high performance Pb-free alternatives to Pb-based piezoelectric ceramics. Although pure BaTiO₃ material exhibits fairly low piezoelectric coefficients, further designing of such a material system greatly enhances the piezoelectric response by means of domain engineering, defects engineering, as well as phase boundary engineering. Especially after the discovery of a Ba(Zr_0.2Ti_0.8)O₃–x(Ba_0.7Ca_0.3)TiO₃ system with extraordinarily high piezoelectric properties (d₃₃ > 600 pC/N), BaTiO₃-based piezoelectric ceramics are considered as one of the promising Pb-free substitutes. In the present contribution, we summarize the idea of designing high property BaTiO₃ piezoceramic through domain engineering, defect-doping, as well as morphotropic phase boundary (MPB). In spite of its drawback of low Curie temperature, BaTiO₃-based piezoelectric materials can be considered as an excellent model system for exploring the physics of highly piezoelectric materials. The relevant material design strategy in BaTiO₃-based materials can provide guidelines for the next generation of Pb-free materials with even better piezoelectric properties that can be anticipated in the near future.