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arXiv · 2602.17382

Data-Driven Prediction of Dielectric Anisotropy in Nematic Liquid Crystals

Charles Parton-Barr·Richard J. Mandle
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Abstract

We curate a large-scale dataset of low frequency dielectric anisotropy values for low molecular weight liquid crystals. Using this dataset, we demonstrate that supervised machine-learning models can predict dielectric anisotropy with substantially improved accuracy (RMSE 2.6) compared to estimates obtained from the Maier-Meier relations using molecular properties from both the widely used semiempirical AM1 method (RMSE 9.7) and the modern r2scan-3c composite method (RMSE 11.2). Realising the potential of machine learning techniques for liquid crystalline materials requires carefully curated data to be accessible, and on this basis we propose a simple and standard template for reporting data.

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Charles Parton-Barr, Richard J. Mandle. 2026-02-19. Data-Driven Prediction of Dielectric Anisotropy in Nematic Liquid Crystals. https://arxiv.org/abs/2602.17382

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