Spotlight: New Research Ranked Among Top AI Papers
Symbolic regression is the ML task of discovering tractable mathematical expressions to fit a dataset, yet the AI community has not fully explored deep learning approaches that explore this challenging space. In a paper accepted as an Oral Presentation at the upcoming International Conference on Learning Representations (ICLR), an LLNL research team proposes a framework that leverages deep reinforcement learning for symbolic regression via a simple idea—use a large model (neural network) to search the space of small models (expressions). With an Oral acceptance rate of only 1.5%, the team’s...
Spotlight: Research Team Recognized for COVID-19 Model
A machine learning model developed by a team of LLNL scientists to aid in COVID-19 drug discovery efforts was a finalist for the Gordon Bell Special Prize for High Performance Computing-Based COVID-19 Research. Using the Sierra supercomputer, the team created a more accurate and efficient generative model to enable COVID-19 researchers to produce novel compounds that could possibly treat the disease.
The team trained the model on an unprecedented 1.6 billion small molecule compounds and 1 million additional promising compounds for COVID-19, which reduced the model training time from 1 day to...
Spotlight: Special Recognition for Researchers
Since joining LLNL as a postdoc in 2013, Jayaraman Thiagarajan’s research has grown to include multiple related fields. This exploration ranges from deep learning–based graph analysis to machine learning (ML) and artificial intelligence (AI) solutions for computer vision, healthcare, language modeling, and scientific applications. Thiagarajan recently received an LLNL Director’s Early Career Recognition Award for his authoritative work and key contributions. He earned a PhD in Electrical Engineering from Arizona State University.
Peer-Timo Bremer has accepted the role as LLNL’s Point of...
3D Printing Meets Machine Learning
Two-photon lithography (TPL)—a widely used 3D nanoprinting technique that uses laser light to create 3D objects—has shown promise in research applications but has yet to achieve widespread industry acceptance due to limitations on large-scale part production and time-intensive setup.
LLNL scientists and collaborators are using machine learning (ML) to address two key barriers to industrialization of TPL: monitoring of part quality during printing and determining the right light dosage for a given material. The team developed an ML algorithm trained on thousands of video images of TPL builds...
Spotlight: Mentoring the Next Generation
For the second year in a row, the DSI teamed up with the University of California at Merced to offer a two-week Data Science Challenge at the beginning of June. The intensive program provided mentors, assignments, virtual tours, and seminars. Under the direction of LLNL’s Marisol Gamboa and UC Merced’s Suzanne Sindi, 21 students worked from their homes through video conferencing and chat programs to develop machine learning (ML) models capable of differentiating potentially explosive materials from other types of molecules.
The UC Merced students were divided into five teams, each led by a...
Spotlight: Materials Science Meets AI
LLNL scientists have taken a step forward in the design of future materials with improved performance by analyzing its microstructure using AI. The work recently appeared in the journal Computational Materials Science.
Technological progress in materials science applications spanning electronic, biomedical, alternate energy, electrolyte, catalyst design, and beyond is often hindered by a lack of understanding of complex relationships between the underlying material microstructure and device performance. But AI-driven data analytics provide opportunities that can accelerate materials design...
