Artificial Intelligence, High Performance Computing

$60K Scialog Award Fuels LLNL Automation Research

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June 28, 2026 by Ivy Delaney

Lawrence Livermore National Laboratory (LLNL) scientist Johanna Schwartz has been awarded $60,000 through the Scialog: Automating Chemical Laboratories initiative to further research into laboratory automation. The funding indicates a focused investment in increasing efficiency and potentially changing methods within chemical research. This award arrives as LLNL demonstrates a sustained commitment to translating research into commercial applications; two of its teams recently participated in the Department of Energy’s Energy I-Corps Cohort 22, continuing a decade of entrepreneurial training. LLNL is also expanding its external collaborations, with eight researchers recently recognized for their work in Strategic Partnership Projects aimed at raising the Lab’s profile.

LLNL Teams Participate in DOE’s Energy I-Corps Cohort

This dedication to commercializing mission innovation extends beyond basic research, signaling an effort to foster economic impact from federally funded science. LLNL’s Innovation and Partnerships Office plays a crucial role in identifying pathways to transition technologies, actively engaging external partners and negotiating agreements necessary for moving innovations beyond the laboratory. Successful scale-up of energy technology, the Lab recognizes, will depend heavily on forging strong partnerships, particularly with industry stakeholders. This funding specifically supports efforts to increase efficiency within laboratory settings, potentially leading to new technologies in chemical analysis and experimentation. LLNL’s approach to technology transfer is further underscored by its partnership with the Fannie and John Hertz Foundation, allowing up to three Hertz Fellows annually to participate in the National Labs Entrepreneurship Academy, cultivating a new generation of science-based entrepreneurs.

VIPS Database Facilitates DOE Intellectual Property Searches

The increasing volume of intellectual property originating from Department of Energy national laboratories previously presented a challenge for those seeking to license or collaborate on federally funded research. Now, the DOE’s Office of Technology Commercialization has launched the Visual Intellectual Property Search database, known as VIPS, designed to streamline searches and reveal technologies developed across 17 national laboratories and associated sites. This centralized resource aims to improve access to a broad range of innovations, from advanced computing to quantum materials, facilitating potential partnerships and commercialization efforts. Lawrence Livermore National Laboratory is actively contributing to and benefiting from this new system, alongside a sustained decade-long commitment to entrepreneurial training demonstrated by two teams attending the DOE’s Energy I-Corps Cohort 22. This focus extends beyond simply generating inventions; LLNL researchers are also being recognized for collaborative efforts, with eight individuals recently honored for work in Strategic Partnership Projects.

The Scialog: Automating Chemical Laboratories initiative has awarded Lawrence Livermore National Laboratory (LLNL) scientist Johanna Schwartz $60,000 to pursue automa

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Ivy Delaney

Ivy Delaney has been working with neural networks and machine learning since the mid-nineties, back when a couple of hidden layers and a long afternoon of training counted as ambitious. She has watched the field go from academic curiosity to the thing quietly running underneath everything, and she brings that long view to quantum computing. For Quantum Zeitgeist she covers the ground where the two fields meet. That means quantum machine learning and the variational algorithms it leans on, and it also means the less glamorous but more interesting story of classical machine learning already doing real work inside quantum machines, decoding error-correcting codes, calibrating noisy hardware and learning the error models that simulators depend on. She writes about the hardware those algorithms have to run on too, and about the post-quantum cryptography scramble that the same hardware has set off. Her stories typically start with the paper, whether that is peer-reviewed work, conference proceedings or an arXiv preprint, with the source linked so you can hold a claim up against the research it came from. She is unimpressed by benchmarks that will not say what they beat, and by demonstrations that only work in the press release.

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