Artificial intelligence accelerates the development of advanced heat-dissipating polymers
Peer-Reviewed Publication
Updates every hour. Last Updated: 30-Oct-2025 00:11 ET (30-Oct-2025 04:11 GMT/UTC)
A machine learning method developed by researchers from Institute of Science Tokyo, the Institute of Statistical Mathematics, and other institutions accurately predicts liquid crystallinity of polymers with 96% accuracy. They screened over 115,000 polyimides and selected six candidates with a high probability of exhibiting liquid crystallinity. Upon successful synthesis and experimental analyses, these liquid crystalline polyimides demonstrated thermal conductivities up to 1.26 W m⁻1 K⁻1, accelerating the discovery of efficient thermal materials for next-generation electronics.
The quantum metric—a key measure of the quantum distance in solid-state materials—helps determine the electronic properties of solids, such as transport phenomena. While scientists have measured the quantum metric directly in artificial systems, its determination in solids has proven challenging. Recently, researchers from Yonsei University have obtained this quantity using photoemission measurements in black phosphorus, furthering theoretical as well as experimental quantum physics.
Research shows that while connections between innovations speed discovery, they also sharply increase the risk of total system collapse – with the sweet spot for sustainable innovation proving surprisingly narrow.
A joint research team from Japan has observed "heavy fermions," electrons with dramatically enhanced mass, exhibiting quantum entanglement governed by the Planckian time – the fundamental unit of time in quantum mechanics. This discovery opens up exciting possibilities for harnessing this phenomenon in solid-state materials to develop a new type of quantum computer.