Graph-based AI model maps the future of innovation
An AI method developed by Professor Markus Buehler finds hidden links between science and art to suggest novel materials.
An AI method developed by Professor Markus Buehler finds hidden links between science and art to suggest novel materials.
Researchers are leveraging quantum mechanical properties to overcome the limits of silicon semiconductor technology.
By analyzing X-ray crystallography data, the model could help researchers develop new materials for many applications, including batteries and magnets.
The startup Striv, which went through MIT’s START.nano accelerator program, has developed a shoe sole for athletes that can track force, movement, and form.
Analysis and materials identified by MIT engineers could lead to more energy-efficient fuel cells, electrolyzers, batteries, or computing devices.
An MIT team uses computer models to measure atomic patterns in metals, essential for designing custom materials for use in aerospace, biomedicine, electronics, and more.
The approach could help engineers design more efficient energy-conversion systems and faster microelectronic devices, reducing waste heat.
The technique characterizes a material’s electronic properties 85 times faster than conventional methods.
Ashutosh Kumar, a materials science and engineering PhD student and MathWorks Fellow, applies his eclectic skills to studying the relationship between bacteria and cancer.
Innovative AI system from MIT CSAIL melds simulations and physical testing to forge materials with newfound durability and flexibility for diverse engineering uses.