||An emerging trend in materials discovery is to infer material properties of interest that are challenging to measure or predict via the consideration of other, more easily quantifiable parameters. This approach opens up exciting opportunities for rapid material screening and thus the discovery of new, optimised materials through the application of high-throughput or low-cost evaluation methods. Such inference methods might rely on experimentally-established correlations, physics-based models, machine learning, or some combination of these tools. Recently examples have appeared across a broad range of disciplines, from structural to functional materials, and materials for energy harvesting and storage. The goal of this symposium is to bridge these different disciplines and provide a common forum for researchers using property inference. Specifically, contributions are solicited that explore and exploit property-property correlations using either simulations, experiments, or both. Some examples are coupling between transport, functional, and mechanical properties, as well as machine learning of property correlations from scant multi-property experimental data. Applications where the throughput and speed of indirect property inference significantly improves upon the speed of direct property measurements/predictions are particularly welcome. Contributions from a broad range of disciplines are encouraged, for example topological insulators, novel 2D semi-conductors, thin film PV cells, density of states in thermal energy storage, and property evolution in nuclear materials.
Specific topics include, but are not limited to:
• Inference of microstructural/property evolution based on advanced characterisation applied to (multi-) property measurements.
• Computationally guided materials discovery where inference is used to identify new compositions/microstructures with desirable properties.
• Machine-learning-based property prediction based on diverse, sparse experimental data.
• The cross-verification of computational and machine learning methods with targeted experimentation.
• Use of inference for the understanding/discovery of functional materials, materials for energy generation, energy storage, and extreme environments.
• Time- and space-resolved methods for the generation of dense experimental data sets.