Working to automate nuclear plant operations
PhD student Lauren Fortier is building on the experience she gained operating a nuclear plant for the Navy to solve a critical hurdle in the wider adoption of the energy source.
Department of Energy (DoE)
MIT projects selected for funding under US Department of Energy’s Genesis Mission
Initial research projects advance national priorities across natural resources, manufacturing, nuclear physics, and more.
Jesse Thaler named director of the Laboratory for Nuclear Science
The professor of physics and inaugural director of the NSF AI Institute for Artificial Intelligence and Fundamental Interactions will lead LNS and continue his research in particle physics.
MIT researchers use AI to uncover atomic defects in materials
A new model measures defects that can be leveraged to improve materials’ mechanical strength, heat transfer, and energy-conversion efficiency.
Working to eliminate barriers to adopting nuclear energy
Nuclear waste continues to be a bottleneck in the widespread use of nuclear energy, so doctoral student Dauren Sarsenbayev is developing models to address the problem.
New tool makes generative AI models more likely to create breakthrough materials
With SCIGEN, researchers can steer AI models to create materials with exotic properties for applications like quantum computing.
DoE selects MIT to establish a Center for the Exascale Simulation of Coupled High-Enthalpy Fluid–Solid Interactions
The research center, sponsored by the DoE’s National Nuclear Security Administration, will advance the simulation of extreme environments, such as those in hypersonic flight and atmospheric reentry.
How to more efficiently study complex treatment interactions
A new approach for testing multiple treatment combinations at once could help scientists develop drugs for cancer or genetic disorders.
Robotic probe quickly measures key properties of new materials
Developed to analyze new semiconductors, the system could streamline the development of more powerful solar panels.
With AI, researchers predict the location of virtually any protein within a human cell
Trained with a joint understanding of protein and cell behavior, the model could help with diagnosing disease and developing new drugs.