ArticleMolecular informatics2026
Design of Thermotropic Liquid Crystal Molecules With a Wide Temperature Range for the Liquid Crystal Phase Using Machine Learning.
Article in Molecular informatics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Deep learning directed synthesis of fluid ferroelectric materials.Materials horizons · 2026Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
Thermotropic liquid crystals are functional materials whose phase structures change with temperature. They exhibit mesophases-intermediate states between solid and liquid. There is demand for materials that maintain a stable mesophase over a wide temperature range ΔT
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.