The brain power behind sustainable AI
PhD student Miranda Schwacke explores how computing inspired by the human brain can fuel energy-efficient artificial intelligence.
PhD student Miranda Schwacke explores how computing inspired by the human brain can fuel energy-efficient artificial intelligence.
First-of-its-kind handbook serves as a guide for design safety for civilian nuclear ships.
Twelve START.nano companies competed for the grand prize of nanoBucks to be used at MIT.nano’s facilities.
Assistant Professor Priya Donti’s research applies machine learning to optimize renewable energy.
The approach combines physics and machine learning to avoid damaging disruptions when powering down tokamak fusion machines.
Panel discussions focused on innovation in many forms of energy, then a tour of campus featured student research.
Optimized for generative AI, TX-GAIN is driving innovation in biodefense, materials discovery, cybersecurity, and other areas of research and development.
For physicist Mostafa Fawzy, MIT Open Learning’s OpenCourseWare was a steadfast companion through countless study sessions.
Improved carbon-cement supercapacitors could turn the concrete around us into massive energy storage systems.
The novel design allows the membranes to withstand high temperatures when separating hydrogen from gas mixtures.
Explosive growth of AI data centers is expected to increase greenhouse gas emissions. Researchers are now seeking solutions to reduce these environmental harms.
The new “CRESt” platform could help find solutions to real-world energy problems that have plagued the materials science and engineering community for decades.
A new device concept opens the door to compact, high-performance transistors with built-in memory.
The collaboration has led to new fuels and a variety of other projects to enable clean, safe nuclear energy.
With SCIGEN, researchers can steer AI models to create materials with exotic properties for applications like quantum computing.