By Ashleigh Papp “The dream of predicting a protein shape just from its gene sequence is now a reality,” said Paul Adams, Associate Laboratory Director for Biosciences at Berkeley Lab. For Adams and other structural biologists who study proteins, predicting their shape offers a key to understanding their function and accelerating treatments for diseases like
An X-ray instrument at Berkeley Lab’s Advanced Light Source contributed to a battery study that used an innovative approach to machine learning to speed up the learning curve about a process that shortens the life of fast-charging lithium batteries.
Berkeley Lab scientists have developed a machine learning model that can be used for calculating optical properties of a known structure, and for designing a structure with desired optical properties.
The American Association for the Advancement of Science, the world’s largest general scientific society, today announced that 489 of its members, among them nine scientists at Berkeley Lab, have been named Fellows. This lifetime honor, which follows a nomination and review process, recognizes scientists, engineers, and innovators for their distinguished achievements in research and other disciplines toward the advancement or applications of science.
If you’ve eaten vegan burgers that taste like meat or used synthetic collagen in your beauty routine – both products that are “grown” in the lab – then you’ve benefited from synthetic biology. It’s a field rife with potential, as it allows scientists to design biological systems to specification, such as engineering a microbe to produce a cancer-fighting agent. Yet conventional methods of bioengineering are slow and laborious, with trial and error being the main approach.
Researchers across the world have already amassed tons of info about COVID-19, and learn more every day. Now, Berkeley Lab experts are developing a platform that puts all this valuable knowledge in one place, and leverages machine learning to make new discoveries.
One of the many unanswered scientific questions about COVID-19 is whether it is seasonal like the flu – waning in warm summer months then resurging in the fall and winter. Now scientists at Lawrence Berkeley National Laboratory are launching a project to apply machine-learning methods to a plethora of health and environmental datasets, combined with high-resolution climate models and seasonal forecasts, to tease out the answer.
A team of materials scientists at Berkeley Lab – scientists who normally spend their time researching things like high-performance materials for thermoelectrics or battery cathodes – have built a text-mining tool in record time to help the global scientific community synthesize the mountain of scientific literature on COVID-19 being generated every day.
In a machine learning challenge dubbed the 2020 Large Hadron Collider Olympics, a team of cosmologists from Berkeley Lab developed a code that best identified a mock signal hidden in simulated particle-collision data.
A team of researchers at Berkeley Lab and UC Berkeley has successfully demonstrated how machine-learning tools can improve the stability of light beams’ size for science experiments at a synchrotron light source via adjustments that largely cancel out unwanted fluctuations.