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.

What happens when things combine? This question lies at the heart of the Borell-Brascamp-Lieb inequality (BBL), a mathematical relation widely applied across many fields of mathematics, science and beyond. Now, researchers from the Okinawa Institute of Science and Technology (OIST), University of Tokyo and University of Florence have provided a new proof for this powerful inequality, taking an unconventional approach based on heat and diffusion equations.

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.

Wireless power transfer (WPT) systems deliver electricity without cables but often struggle with voltage stability when loads change. In this study, researchers developed a machine learning-based design method that uses numerical optimization to achieve load-independent operation. Their approach ensures stable output voltage (fluctuations under 5%) and high efficiency (86.7%) under varying loads. This breakthrough simplifies WPT design and could help create more practical, reliable wireless power systems for a wide range of applications.

Researchers from Japan successfully parallelized both the backward narrowing and transition subsumption steps in the powerful analyzer Maude-NPA for cryptographic protocol analysis