With up to $53 million from ARPA-H, the researchers will use AI to generate safer herbicides that reduce dependence on imports and improve the security of the U.S. food supply.
A machine-learning approach called CheMeleon provides a practical way to develop new drugs or other molecules with much less detailed, expensive experimentation.
Read MoreMIT engineers have found a way to stabilize the lipid nanoparticles used to deliver RNA vaccines, which could allow the vaccines to be more widely distributed.
Read MoreThe “CrysVCD” tool developed at MIT could cut the huge amounts of time and money spent on screening out chemically unstable designs.
Read MoreInitial research projects advance national priorities across natural resources, manufacturing, nuclear physics, and more.
Read MoreMIT researchers’ DiffSyn model offers recipes for synthesizing new materials, enabling faster experimentation and a shorter journey from hypothesis to use.
Read MoreAI supports the clean energy transition as it manages power grid operations, helps plan infrastructure investments, guides development of novel materials, and more.
Read MoreSystem developed at MIT could provide realistic predictions for a wide variety of reactions, while maintaining real-world physical constraints.
Read MoreSolubility predictions could make it easier to design and synthesize new drugs, while minimizing the use of more hazardous solvents.
Read MoreThe Initiative for New Manufacturing is convening experts across the Institute to drive a transformation of production across the U.S. and the world.
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