EPFL researchers have developed Saturn and TANGO, two complementary AI frameworks that make generative molecular design faster, more efficient, and closer to real laboratory chemistry.

Through a collaboration between the EPFL Center for Quantum Science and Engineering (QSE) and SCITAS, EPFL has become the first Swiss academic institution to establish a virtual platform offering advanced quantum computing capabilities to its researchers.

EPFL researchers have used a neural network to create all-atom models of proteins, as well as the dynamic movements that govern their function. Their comprehensive yet simplified approach resolves a major bottleneck in biology.

Gaps in data and difficult‑to‑compare datasets limit what climate and weather AI models can reliably predict. Researchers from the ETH Domain have now introduced an AI model that helps close these gaps, reconstructs satellite images, and sheds light on how weather, land and water interact.

An EPFL-led study shows how language models can guide chemical synthesis and explain reactions using plain-language instructions.

Traffic jams are a problem in large urban areas. How can they be solved without expanding the road network? This is a challenge that researchers are attempting to address.