Hybrid organic-inorganic materials consist of inorganic frameworks hosting dipolar organic cations. They offer a vast design space of flexible, solution-processable and potentially non-toxic materials for a range of application including photovoltaics, piezoelectrics and ferroelectrics. We are particularly interested in ferro- and piezoelectric materials, and how substitutional and orientational disorder can enhance their response properties.

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Solid electrolytes offer a safer alternative to liquid electrolytes in batteries, but their ionic conductivity is too low for commercialisation. We are working on designing novel solid electrolytes based on computational chemistry and machine learning methods, while also diving into to the fundamental origin of fast ionic conductivity. We are particularly intrigued by how substitutional disorder can enhance the transport properties of solid electrolytes.

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Disorder in crystalline materials is often responsible for improved (or deteriorated) materials performance, but it is commonly overlooked in computational materials design. Similarly, vibrational entropic effects can stabilise materials at elevated temperatures. We apply and develop methods to tackle disorder and vibrations in crystalline materials and allow for faster and more accurate predictions of materials stability and properties at more realistic conditions.

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Materials design based on first principles calculations can accelerate materials discovery, but it is still too slow to fully explore the vast materials design space – especially when disorder, defects and finite temperature effects are included. We are working on developing machine learning accelerated predictions of finite temperature materials stability and properties, and on developing machine learning force fields for accelerated molecular dynamics simulations, e.g. of ion transport.

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