Abstract
The development of advanced materials will increasingly rely on our ability to assemble complex compositions in an ordered and predictable manner to generate enhanced properties. It is attractive to harness the ever-increasing power of computation in the search for new materials, but the scale and nature of the problem make brute force de novo approaches challenging, while "big data" searches for analogues of existing structures in databases cannot identify potentially transformative new structures. Building chemical knowledge into computational tools used together with experiment offers a different approach. I will present an example of crystal chemically-informed computational identification of a new solid oxide fuel cell cathode [1]. This integrated approach has recently allowed us to combine permanent magnetism and electrical polarisation in a single phase material above room temperature [2], a major challenge in materials synthesis because of the competing electronic structure requirements of these two ground states. As a counterpoint, we have recently used a noncomputational multiple length scale symmetry control strategy to switch both of these long-range orders in a magnetoelectric multiferroic at room temperature [3]. This emphasises the enduring importance of developing the crystal chemical understanding that drives "classical" approaches to materials design.
