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.
The “CrysVCD” tool developed at MIT could cut the huge amounts of time and money spent on screening out chemically unstable designs.
Initial research projects advance national priorities across natural resources, manufacturing, nuclear physics, and more.
MIT researchers’ approach captures subtle atomic patterns, improving predictions of material properties.
Connor Coley works at the interface of chemistry and machine learning, to discover and design new drug compounds.
MIT researchers’ DiffSyn model offers recipes for synthesizing new materials, enabling faster experimentation and a shorter journey from hypothesis to use.
AI supports the clean energy transition as it manages power grid operations, helps plan infrastructure investments, guides development of novel materials, and more.
System developed at MIT could provide realistic predictions for a wide variety of reactions, while maintaining real-world physical constraints.
Solubility predictions could make it easier to design and synthesize new drugs, while minimizing the use of more hazardous solvents.
The Initiative for New Manufacturing is convening experts across the Institute to drive a transformation of production across the U.S. and the world.
The MIT Energy Initiative’s annual research symposium explores artificial intelligence as both a problem and a solution for the clean energy transition.