The need for new materials to tackle societal challenges in energy and sustainability is
widely acknowledged. As demands for performance increase while resource constraints
narrow available options, the vastness of composition, structure and process parameter
space make the apparently simple questions of where to look for and how to then find the
materials we need a grand challenge to contemporary physical science. This talk will
emphasise that discovery synthesis of new inorganic materials is at the extreme forefront of
this endeavour.
In this presentation, I will address the role of digital and robotic tools in discovery from the
perspective of the experimental realisation of new materials with structures that differ from
those in the databases (Nature 546, 280, 2017) in a manner that has consequence for their
functional performance (Angewandte Chemie Int Ed 60, 16457,2021). This will include the
demonstration that it is now possible under clear assumptions to guarantee to predict the
crystal structure of a material based solely on its composition (Nature 619, 68, 2023), the
role of machine learning from data in supporting decisions by experimental researchers
(Nature Communications 12, 5561, 2021; J. Am. Chem. Soc. 144, 22178, 2022), and the
acceleration of inorganic materials discovery with robots (Chemical Science 15, 2640, 2024).
The role of these digital tools in a modern integrated materials discovery workflow will be
presented with an example of the discovery (i.e., the experimental realisation in the
laboratory) of a quaternary inorganic solid that displays high lithium ion conductivity that
arises from its new structure. This leads to a different perspective on how lithium ions can
attain high mobility in solids (Science, 2024). Such perspectives may prove generally helpful
in the design of the fast ion transporting materials that we will need across future energy
technologies.
Location:
373 Cory Hall