In continual learning (CL), we incrementally adapt a model to learn tasks (defined according to the problem at hand) observed sequentially. CL has two main objectives: maintain long-term memory (remember previous tasks) and navigate new experiences continually (quickly adapt to newtasks).

Members of the GenSLMs team received the Gordon Bell Special Prize for HPC-Based COVID-19 Research at the SC22 conference. Scientists from Argonne National Laboratory and a team of collaborators have won the 2022 ACM Gordon Bell Special Prize for High Performance Computing-Based COVID-19 Research for their method of quickly identifying how a virus evolves. Their work in training large language models (LLMs) to discover variants of SARS-CoV-2 has implications to biology beyond COVID-19. The researchers leveraged Argonne's supercomputing and AI resources to develop and apply LLMs toward tracking how a virus can mutate into more dangerous or more transmissible variants, or a variant of concern (VOC). Existing methods to track VOCs can be slow.

Making the countless numbers of N95 masks that have protected millions of Americans from COVID requires a process that not only demands attention to detail but also requires lots of energy. Many of the materials in these masks are produced by a technique called melt blowing, in which tiny plastic fibers are spun at high temperatures that necessitate the use of a lot of energy. The process is also used for other products like furnace filters, coffee filters and diapers. Thanks to a new computational effort being pioneered by the U.S. Department of Energy's (DOE) Argonne National Laboratory in conjunction with 3M and supported by the DOE'S High Performance Computing for Energy Innovation (HPC4EI) program, researchers are finding new ways to dramatically reduce the amount of energy required for melt blowing the materials needed in N95 masks and other applications. Currently, the process used to create a nozzle to spin nonwoven materials produces a very high-quality product, but it is quite energy intensive.

Physics-based simulations, that staple of traditional HPC, may be evolving toward an emerging, AI-based technique that could radically accelerate simulation runs while cutting costs. Called "surrogate machine learning models," the topic was a focal point in a keynote on Tuesday at the International Conference on Parallel Processing by Argonne National Lab's Rick Stevens. Stevens, ANL's associate laboratory director for computing, environment and life sciences, said early work in "surrogates," as the technique is called, shows tens of thousands of times (and more) speed-ups and could "potentially replace simulations." In his keynote, entitled, "Exascale and Then What?: The Next Decade for HPC and AI," Stevens explained surrogates this way: "You have a system, it could be a molecular system or drug design…, and you have a physics-based simulation of it… You run this code and capture the input-output relationships of the core simulation… You use that training data to build an approximate model. These are typically done with neural networks… and this surrogate model approximates the simulation, and typically it is much faster. Of course, it has some errors, so then you use that surrogate model to search the space, or to advance time steps. And then maybe you do a correction step later."

Near-real-time data on avian-solar interactions will help the energy industry understand risks and opportunities for wildlife at solar energy plants. How does an array of solar panels change a habitat? The question is complex--and increasingly important, as solar energy plants proliferate across the United States. The industry and researchers, however, currently don't have a lot of answers. Researchers at the Department of Energy's (DOE) Argonne National Laboratory are developing technology that can help.

It will be doing so for billions more years. Yet, we have only just begun tapping into that abundant, renewable source of energy at affordable cost. Solar absorbers are a material used to convert this energy into heat or electricity. Maria Chan, a scientist in the U.S. Department of Energy's (DOE) Argonne National Laboratory, has developed a machine learning method for screening many thousands of compounds as solar absorbers. Her co-author on this project was Arun Mannodi-Kanakkithodi, a former Argonne postdoc who is now an assistant professor at Purdue University.

Team's algorithm could lead to pivotal discovery of new materials for solar cells. It will be doing so for billions more years. Yet, we have only just begun tapping into that abundant, renewable source of energy at affordable cost. Solar absorbers are a material used to convert this energy into heat or electricity. Maria Chan, a scientist in the U.S. Department of Energy's (DOE) Argonne National Laboratory, has developed a machine learning method for screening many thousands of compounds as solar absorbers.

Unlike fossil fuel-fired power plants, nuclear power plants provide large amounts of low-carbon electricity. But the expense of running these plants has made it difficult for them to stay open. Researchers at the U.S. Department of Energy's (DOE) Argonne National Laboratory are building systems that could make nuclear energy more competitive using artificial intelligence. Argonne is midway through a $1 million, three-year project to explore how smart, computerized systems could change the economics. Funded by the DOE Office of Nuclear Energy's Nuclear Energy Enabling Technologies program, the project aims to create a computer architecture that could detect problems early and recommend appropriate actions to human operators.

Artificial intelligence (AI) is the collective term for computer technologies and techniques that help solve complex problems by imitating the brain's ability to learn. AI helps computers recognize patterns hidden within a lot of information, solve problems and adjust to changes in processes as they happen, much faster than humans can. Researchers use AI to be better and faster at tackling the most difficult problems in science, medicine and technology, and help drive discovery in those areas. This could range from helping us understand how COVID-19 attacks the human body to finding ways to manage traffic jams. Many Department of Energy (DOE) facilities, like Argonne National Laboratory, assist in developing some the most advanced AI technologies available.

Argonne scientists are building systems to streamline operations and maintenance at reactors. Nuclear power plants provide large amounts of electricity without releasing planet-warming pollution. But the expense of running these plants has made it difficult for them to stay open. If nuclear is to play a role in the U.S. clean energy economy, costs must come down. Scientists at the U.S. Department of Energy's (DOE) Argonne National Laboratory are devising systems that could make nuclear energy more competitive using artificial intelligence.