Gen AI writes code, forecasts weather, develops new materials

Researchers at Sandia National Laboratories aim to automatically generate code in Kokkos, a parallel programming language designed for use across many of the world’s largest supercomputers.

The specialised language, developed by researchers from several national labs, handles the nuances of running tasks across tens of thousands of processors.

Sandia is employing retrieval-augmented generation (RAG) to create and link a Kokkos database with AI models. As researchers experiment with different RAG approaches, initial tests show promising results.

Cloud-based services like NeMo Retriever are among the RAG options the scientists will evaluate.

Building copilots via model tuning and RAG is just a start. Researchers eventually aim to employ foundation models trained with scientific data from fields such as climate, biology and material science.

Researchers and companies in weather forecasting are embracing CorrDiff, a generative AI model that’s part of NVIDIA Earth-2, a set of services and software for weather and climate research.

CorrDiff can scale the 25km resolution of traditional atmosphere models down to 2 kilometers and expand by more than 100x the number of forecasts that can be combined to improve confidence in predictions.

“We plan to leverage such models in our global and regional AI forecasts for richer insights,” says Spire’s Tom Gowan.

Switzerland-based Meteomatics recently announced it also plans to use NVIDIA’s generative AI platform for its weather forecasting business.

At Argonne National Laboratory, scientists are using the technology to generate gene sequences that help them better understand the virus behind COVID-19. Their models, called GenSLMs, spawned simulations that closely resemble real-world variants of SARS-CoV-2.

“Understanding how different parts of the genome are co-evolving gives us clues about how the virus may develop new vulnerabilities or new forms of resistance,” says researcher Arvind Ramanathan.

GenSLMs were trained on more than 110 million genome sequences with NVIDIA A100 Tensor Core GPU-powered supercomputers, including Argonne’s Polaris system, the U.S. Department of Energy’s Perlmutter and NVIDIA’s Selene.

Microsoft Research’s MatterGen model generates novel, stable materials that exhibit desired properties. The approach enables specifying chemical, magnetic, electronic, mechanical and other desired properties.

“We believe MatterGen is an important step forward in AI for materials design,” the Microsoft Research team wrote of the model they trained on Azure AI infrastructure with NVIDIA A100 GPUs.