Machine Learning for Science
Machine Learning Takes on Synthetic Biology
Berkeley Lab scientists develop a tool that could drastically speed up the ability to design new biological systems
September 25, 2020
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. However, the field is rife with possibilities that reach beyond burgers and face creams and into the realms of medicine, agriculture, climate, energy, and materials. Berkeley Lab scientists are developing smart algorithms with the potential to reduce bioengineering development cycles from years down to months, or even weeks.
About Machine Learning at Berkeley Lab ⤓
Machine learning is a promising branch of artificial intelligence that Berkeley Lab scientists develop and employ in hundreds of projects every day. Our researchers track atomic particles, search for better battery materials, analyze traffic patterns, improve crop yields, pinpoint extreme weather in exascale climate simulations, and piece together metagenomic puzzles from billions of DNA fragments using tools, technology, and advanced mathematics, much of it developed by Berkeley Lab scientists. ⤓ Scroll down for more.
A Powerful Scientific Tool
Machine learning is a branch of artificial intelligence that makes inferences from raw data using sophisticated algorithms and powerful computers. For online shoppers, that means better "you might also like..." suggestions. But for scientists, machine learning tools can reveal profound insights hiding in ballooning datasets.
Thanks to better instruments, including technologies developed at Berkeley Lab, we can see things at a microscopic and atomic scale, measure vibrations imperceptible to the human eye, and capture high-resolution images of objects millions of light years away. But those instruments produce vastly larger datasets than ever. The Large Synoptic Survey Telescope (LSST) will produce 20 terabytes of data every night, about 60 petabytes over its lifetime. The Large Hadron Collider has already produced 900 petabytes of data (50 petabytes in 2018 alone) and expects to create another 500 petabytes by 2024. Conventional data analysis alone can't keep up.
Using machine learning techniques, models can be automatically derived from that data. These models can be used to identify features, reduce complexity, and control experiments.
Math, Software & Tools to Spur Innovation
Berkeley Lab's research into machine learning builds on its foundational work in mathematics to develop methods that are are consistent with physical laws, robust in the presence of noisy or biased data, and capable of being interpreted and explained in scientifically meaningful ways.
As a Department of Energy National Laboratory, we develop and share the algorithms, software, tools and libraries that are foundational to scientific machine learning. We gather, organize and store huge scientific datasets in areas such as materials, energy, environment, biology, genomics, and astronomy. And we develop tools and advanced networking facilities to make these datasets more searchable and accessible.