Machine learning (ML) has emerged as a transformative tool in polymer science, enabling researchers to predict material properties and guide polymer design with unprecedented speed and precision.

Gas sensing material screening faces challenges due to costly trial-and-error methods and the complexity of multi-parameter ...

Magnetic materials are in high demand. They're essential to the energy storage innovations on which electrification depends and to the robotics systems powering automation. They're also inside more ...

Rainfall prediction has advanced rapidly with the adoption of machine learning, but most models remain optimized for overall ...

Electrical power systems engineers need practical methods for predicting solar output power under varying environmental conditions of a single panel. By integrating an Arduino-based real-time data ...

Scientific knowledge advances through the interplay of empiricism and theory. Empirical observations of environmental ...

Filling gaps in data sets or identifying outliers—that's the domain of the machine learning algorithm TabPFN, developed by a ...

The ability to anticipate what comes next has long been a competitive advantage -- one that's increasingly within reach for developers and organizations alike, thanks to modern cloud-based machine ...