ArticleMaterials horizons2026
Deep learning directed synthesis of fluid ferroelectric materials.
Article in Materials horizons, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
We present a data-to-molecule deep-learning workflow for the targeted design of organic fluid ferroelectrics and validate it through the synthesis and experimental characterisation of machine-learning generated candidate materials. Fluid ferroelectrics, a class of liquid crystals that exhibit switchable long-range polar order, offer opportunities in ultrafast electro-optic technologies, responsive soft matter, and next-generation energy materials. Without well-validated design rules, development of new fluid ferroelectric materials remains challenging. We curate a comprehensive dataset of all known longitudinally polar liquid-crystal materials and train graph neural networks to predict ferroelectric nematic transition temperatures. A graph variational autoencoder generates
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