Ernest Orlando Lawrence. Image credit: Energy.gov
To celebrate 70 years of advancing scientific knowledge, OSTI is featuring some of the leading scientists and works particularly relevant to the formation of DOE and OSTI, and highlighting Nobel Laureates and other important research figures in DOE’s history. Their accomplishments were key to the evolution of the Department of Energy, and OSTI’s collections include many of their publications.
Ernest Orlando Lawrence’s love of science began at an early age and continued throughout his life. His parents and grandparents were educators and encouraged hard work and curiosity. While working on his Bachelor of Arts degree in chemistry at the University of South Dakota and thinking of pursuing a career in medicine, Lawrence became influenced by faculty mentors in the field of physics and decided instead to pursue his graduate degree in physics at the University of Minnesota. After completing his Master’s degree, he studied for a year at the University of Chicago, where, Lawrence “caught fire as a researcher,” in the words of a later observer. After Lawrence earned his Ph.D. in physics at Yale University in 1925, he stayed on for another three years as a National Research Fellow and an assistant professor of physics. In 1928, Lawrence was recruited by the University of California, Berkeley as associate professor of physics. Two years later, at the age of 27, he became the youngest full professor at Berkeley.
Image Credit: iStock.com/Henrik5000
If you have used your cell phone’s personal assistant to help you find your way or taken advantage of its translation or speech-to-text programs, then you have benefitted from a deep learning neural network architecture. Inspired by the human brain’s ability to learn, deep learning neural networks are based on a class of machine algorithms that can learn to find patterns and closely represent those patterns at many levels. As additional information is received, the network refines those patterns, gains experience, and improves its probabilities, essentially learning from its mistakes. This is called “deep learning” because the networks that are involved have a depth of more than just a few layers.
Basic deep learning concepts were developed many years ago; with today’s availability of high performance computing environments and massive datasets, there has been a resurgence of deep learning neural network research throughout the science community. Scalable tools are being developed to train these networks, and brain-inspired computing algorithms are achieving state-of-the-art results on tasks such as visual object classification, speech and image recognition, bioinfomatics, neuroscience, language modeling, and natural language understanding.
X-ray imaging shows how memristors work at
an atomic scale. Image credit: SLAC National
A tiny device called a memristor holds great promise for a new era of electronics. Unlike a conventional resistor, its resistance can be reset, and it remembers its resistance. It functions in a way that is similar to synapses in the human brain, where neurons pass and receive information. A memristor is a two-terminal device whose resistance depends on the voltages applied to it in the past. When the voltage is turned off, the resistance remains or remembers where it was previously. This little device actually learns. A commercially viable memristor could enable us to move away from flash memory and silicon-based computing to smart energy-efficient computers that operate similarly to the human brain, with the capability to comprehend speech and images, and with highly advanced memory retention.
Image credit: Los Alamos
Condensed matter physicist Albert Migliori has been solving scientific mysteries for national security throughout his career. Migliori is a Los Alamos National Laboratory (LANL) fellow, the Director of the Seaborg Institute for Actinide Science, and a member of the Science Advisory Council at the National High Magnetic Field Laboratory. He is best known for leading development of a technique called resonant ultrasound spectroscopy (RUS), a powerful tool that uses acoustic tones to determine important measurements in condensed matter physics, including superconductivity.
Migliori recently wrote about his love of physics and fascinating career in an “In Their Own Words” column in LANL’s 1663 magazine. Migliori grew up in the 1950s on the Lower West Side of Manhattan, where he learned to rewire lamps and fix everything as a young child. He became interested in physics at a science magnet high school in New York City and received his B.S. from Carnegie Mellon University and his M.S. and Ph.D. in physics from the University of Illinois. He became hooked on what he calls, “hard-core, hands-dirty experimental physics.”
Image Credit: DOE Water Power Program
Movements of waves, tides, and currents in the ocean carry kinetic energy that can be harnessed and converted to electricity. There is vast potential for using this ocean resource to provide clean, renewable energy to communities and cities in coastal areas, and it could impact the nearly half of the U.S. population that lives within 50 miles of the coastlines.
The U.S. Department of Energy (DOE) Water Power Program supports the design, development, testing, and demonstration of marine and hydrokinetic (MHK) technologies that can capture energy from waves, tides, and currents. This program also funds the creation of instrumentation, modeling, and simulation tools to enable real-condition testing of technologies. DOE recently announced $20 million in funding for projects that advance and monitor marine and hydrokinetic energy systems and will contribute to the development of a commercially viable MHK industry.
The Water Power Program also sponsored the $2.25 million, 20-month Wave Energy Prize challenge. This public contest was designed to encourage the development of more efficient wave energy converter (WEC) devices that would double the energy absorption base line captured from ocean waves, making wave energy more competitive with traditional energy solutions.