How to predict the behavior of molecules from scant data
A machine-learning approach called CheMeleon provides a practical way to develop new drugs or other molecules with much less detailed, expensive experimentation.
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Department of Energy (DoE)
AI helps design new materials that work in the real world
The “CrysVCD” tool developed at MIT could cut the huge amounts of time and money spent on screening out chemically unstable designs.
Read MoreMIT 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.
Read MoreJesse 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.
Read MoreMIT 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.
Read MoreWorking 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.
Read MoreWith SCIGEN, researchers can steer AI models to create materials with exotic properties for applications like quantum computing.
Read MoreDOE 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.
Read MoreHow 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.
Read MoreDeveloped to analyze new semiconductors, the system could streamline the development of more powerful solar panels.
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