Understanding material transformations at the microscopic level is crucial for advancing technology. Researchers from Japan have recently studied liquid crystal phase transitions, revealing the complex dynamics behind their shape-shifting behavior. Using a combination of computer simulations and machine learning, they uncovered how liquid crystals form twin boundaries during phase shifts. The strategy developed in this study could help gain deeper insight into the transformation dynamics in a wide range of materials.

In a first-of-its-kind breakthrough, a team of UBC Okanagan researchers has developed an artificial adhesion system that closely mimics natural biological interactions.

Dr. Isaac Li and his team in the Irving K. Barber Faculty of Science study biophysics at the single-molecule and single-cell levels. Their research focuses on understanding how cells physically interact with each other and their environment, with the ultimate goal of developing innovative tools for disease diagnosis and therapy.

A group of young students became bonafide biomedical scientists before they even started high school. Through a partnership with a nearby university, the middle schoolers collected and analyzed environmental samples to find new antibiotic candidates. One unique sample, goose poop collected at a local park, had a bacterium that showed antibiotic activity and contained a novel compound that slowed the growth of human melanoma and ovarian cancer cells in lab tests.

The algorithms behind generative AI tools like DallE, when combined with physics-based data, can be used to develop better ways to model the Earth’s climate. Computer scientists in Seattle and San Diego have now used this combination to create a model that is capable of predicting climate patterns over 100 years 25 times faster than the state of the art.

Where do you see patterns in chaos? It has been proven, in the incredibly tiny quantum realm, by an international team co-led by UC Santa Cruz physicist Jairo Velasco, Jr. In the journal Nature, the researchers detail an experiment that confirms a theory first put forth 40 years ago stating that electrons confined in quantum space would move along common paths rather than producing a chaotic jumble of trajectories.

Today, the Polymathic AI team released two of its open-source training dataset collections to the public — a colossal 115 terabytes in total from dozens of sources — for the scientific community to use to train AI models and enable new scientific discoveries. The datasets include data from dozens of sources, including astrophysics, biology, fluid dynamics, acoustics and chemistry. The Polymathic AI team provides further information about the datasets in two papers accepted for presentation at the leading machine learning conference, NeurIPS, to be held in December in Vancouver, Canada.