Paving the way for greener ammonia production

MIT projects selected for funding under US Department of Energy’s Genesis Mission

Initial research projects advance national priorities across natural resources, manufacturing, nuclear physics, and more.

MIT’s Initiative for New Manufacturing builds momentum

In its first year, INM has worked across research, workforce development, and industry engagement to help accelerate new manufacturing technologies and their real-world deployment.

Can Artificial Intelligence Accelerate Technological Progress? Researchers' Perspectives on AI in Manufacturing and Materials Science

arXiv:2511. 14007v3 Announce Type: replace-cross Abstract: Artificial intelligence (AI) raises expectations of substantial increases in rates of technological progress, but such anticipations are often not connected to detailed ground-level studies of AI use in innovation processes.

Wasted large language models: A life cycle thinking approach

arXiv:2608. 17055v1 Announce Type: cross Abstract: Large Language Models (LLMs) are machine learning (ML) models that have an increasingly large carbon footprint through their development and use.

Engineering more resilient crops for a warming climate

Scientists are using AlphaFold to strengthen a photosynthesis enzyme for resilient, heat-tolerant crops.

Solving the solvent problem

By focusing on electrolytes, MIT scientists are making sodium-metal batteries a more practical energy storage option.

Every tree counts

Meta set a goal to reach net zero emissions by 2030. We are developing technology to mitigate our carbon footprint and making these openly available.

Synthetic data: save money, time and carbon with open source

Autonomous computational catalysis through an agentic research system

arXiv:2601. 13508v4 Announce Type: replace-cross Abstract: Autonomous agents are beginning to transform scientific research from tool-assisted workflows toward self-sustaining discovery processes.

Introducing Nano Banana Pro

Generative and multimodal AI for materials prediction and design: Progress, challenges, and perspectives

arXiv:2607. 21660v1 Announce Type: cross Abstract: Artificial intelligence (AI) is accelerating materials prediction and design by enabling efficient exploration of chemical and structural spaces, with particular promise for novel materials discovery.

Strategy-first synthesis planning for complex natural products

arXiv:2608. 07454v1 Announce Type: cross Abstract: The total synthesis of a complex molecule is among the most demanding intellectual and experimental feats in chemistry: a chemist must plan many steps ahead for how to assemble simple building blocks into an intricate target, devise backup strategies, and anticipate procedural challenges.