New MIT research could lead to better materials for a fossil-fuel-free process for making the chemical that's essential to fertilizer and other products.
Atlas Building Composites, a spinout from MIT, is commercializing research that transforms plastic waste into durable building components. The company focuses on producing parts for buildings and other infrastructure using this innovative material conversion process.
Initial research projects advance national priorities across natural resources, manufacturing, nuclear physics, and more.
The article titled "The convergent laboratory: when AI reasoning, autonomous experiments, high performance and quantum computing reshape chemistry" discusses insights from the TPC26 conference, where leaders from academia, national laboratories, and industry examined how AI, autonomous agents, self-driving labs, high‑performance computing, and quantum computing converge to accelerate materials science discovery. It presents firsthand experiences from researchers at the forefront of these technologies and argues that their simultaneous maturation marks a tipping point for transformative advances and productive disruption in chemical sciences.
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.
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.
MIT affiliates collaborate with the MIT‑IBM Computing Research Lab to accelerate the deployment of AI and quantum technologies, applying rigorous theoretical foundations to production systems.
The paper argues that large language models (LLMs) have a growing carbon footprint and that current efficiency gains are offset by rebound effects such as Jevons Paradox. It proposes applying the EU waste hierarchy—prevention, reuse, recycling, recovery, and disposal—to LLMs, suggesting that preventing waste and unnecessary use can significantly reduce environmental impact. The authors emphasize that treating LLMs as products that can become waste offers new ways to motivate more sustainable practices.
By focusing on electrolytes, MIT scientists are making sodium-metal batteries a more practical energy storage option.
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.
The MIT-developed tool "CrysVCD" uses AI to design new materials that are chemically stable in real-world conditions. By predicting stability early, it can significantly reduce the time and cost associated with testing and discarding unstable material designs.
The article discusses how the rapid development of artificial intelligence (AI) and machine learning (ML) is transforming chemical engineering by influencing problem formulation, analysis, and solution across a wide range of applications, from atomic-scale simulations to industrial operations. It highlights recent methodological advances and representative uses, noting a shift from purely black-box models to hybrid and physics-informed frameworks that incorporate conservation laws, thermodynamic consistency, and structural constraints. These integrated approaches enhance robustness, reliability, and human-AI collaboration, ultimately amplifying rather than replacing core chemical engineering principles.