Quantum modeling for breakthroughs in materials science and sustainable energy
Quantum chemist and School of Science Dean’s Postdoctoral Fellow Ernest Opoku is working on computational methods to study how electrons behave.
Quantum chemist and School of Science Dean’s Postdoctoral Fellow Ernest Opoku is working on computational methods to study how electrons behave.
Industry leaders agree collaboration is key to advancing critical technologies.
The MIT Quantum Initiative is taking shape, leveraging quantum breakthroughs to drive the future of scientific and technological progress.
The discovery will help researchers understand how chemicals form and change before stars and planets are born.
Materials from ancient rocks could reveal conditions in the early solar system that shaped the early Earth and other planets.
A leading researcher in protein folding biochemistry and next-generation protein engineering techniques will advance chemistry research and education.
The new dyes are based on boron-containing molecules that were previously too unstable for practical use.
MIT researchers developed a model that explains lithium intercalation rates in lithium-ion batteries.
MIT researchers traced chemical fossils in ancient rocks to the ancestors of modern-day demosponges.
Cache DNA has developed technologies that can preserve biomolecules at room temperature to make storing and transporting samples less expensive and more reliable.
Outfitted with antibodies that guide them to the tumor site, the new nanoparticles could reduce the side effects of treatment.
Researchers develop a fast-acting, cell-permeable protein system to control CRISPR-Cas9, reducing off-target effects and advancing gene therapy.
Solubility predictions could make it easier to design and synthesize new drugs, while minimizing the use of more hazardous solvents.
Lab experiments show “ionic liquids” can form through common planetary processes and might be capable of supporting life even on waterless planets.
Researchers created polymers that are more resistant to tearing by incorporating stress-responsive molecules identified by a machine-learning model.