Checking the quality of materials just got easier with a new AI tool
Acting as a “virtual spectrometer,” SpectroGen generates spectroscopic data in any modality, such as X-ray or infrared, to quickly assess a material’s quality.
Acting as a “virtual spectrometer,” SpectroGen generates spectroscopic data in any modality, such as X-ray or infrared, to quickly assess a material’s quality.
Incorporating machine learning, MIT engineers developed a way to 3D print alloys that are much stronger than conventionally manufactured versions.
MIT researchers developed a model that explains lithium intercalation rates in lithium-ion batteries.
The novel design allows the membranes to withstand high temperatures when separating hydrogen from gas mixtures.
The method enhances 3D bioprinting capabilities, accelerating process optimization for real-world applications in tissue engineering.
The stand-alone PhD program is building connections and preparing students to make a difference.
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.
The findings may redefine how cell identity is established and enable the creation of more sophisticated engineered tissues.
Popular mechanical engineering course applies machine learning and AI theory to real-world engineering design.
Succeeding founding executive director Renee Robins, Giardina will help shape and implement the goals and initiatives of MIT’s eminent water and food program.
New findings could help manufacturers design gels, lotions, or even paving materials that last longer and perform more predictably.
PhD student Erik Ballesteros is building “Doc Ock” arms for future astronauts.
By combining several cutting-edge imaging technologies, a new microscope system could enable unprecedentedly deep and precise visualization of metabolic and neuronal activity, potentially even in humans.
The MRL helps bring together academia, government, and industry to accelerate innovation in sustainability, energy, and advanced materials.
MIT engineers used a machine-learning model to design nanoparticles that can deliver RNA to cells more efficiently.