How have some companies experienced dramatic growth and productivity improvement in manufacturing even as their peers struggle to compete? What explains how some manufacturing firms have been faster to adopt new technologies or workforce practices than other firms? This presentation will focus on understanding the operational and technological patterns of high-performing manufacturing firms in the United States. It will emphasize particularly the way that these firms have built on – and in some cases departed from – the Toyota Production System, which has for decades been the paradigm for manufacturing excellence in the United States and abroad.
The rise of LLMs and generative AI has caused a dramatic increase in the energy consumption of data centers, a problem that will continue to grow as AI becomes more ubiquitous. Our group studies the use of photonics as an enabler for next-generation AI accelerators that can be orders of magnitude faster and more efficient than electronic processors, leveraging the bandwidth, latency, and low-loss interconnection advantages of optically encoded signals. I will discuss our work addressing the main challenges of photonic computing, including (i) scalability, where we are developing time-multiplexed and free-space optical systems to overcome area bottlenecks, (ii) noise and imperfections, where we have developed new hardware error correction algorithms for photonics, (iii) the use of delocalized computing to overcome von Neumann bottlenecks (with additional applications in quantum-secure computation), and (iv) training, where we have demonstrated a forward-only training algorithm for photonic neural networks.
Architected materials—i.e., materials whose three-dimensional (3D) micro- or nanostructure has been engineered to attain a specific purpose—are ubiquitous in nature and have enabled properties that are unachievable by all other existing materials. Their concept relies on maximizing performance while requiring a minimal amount of material. Several human-made 3D architected materials have been reported to enable novel mechanical properties such as high stiffness-to-weight ratios or extreme resilience, especially when nanoscale features present. However, most architected materials have relied on advanced additive manufacturing techniques that are not yet scalable and yield small sample sizes. Additionally, most of these nano- and micro-architected materials have only been studied in controlled laboratory conditions, while our understanding of their performance in real-world applications requires attention.
In this talk, we will explain the concept of architected materials, providing various examples that we routinely fabricate and test in our laboratory at MIT, and we will discuss how nanoscale features significantly enhance their performance. We will also discuss ongoing research directions that will not only allow us to scale-up their fabrication, but also understand how they perform in realistic conditions outside the laboratory—towards contributing to more efficient material solutions in industry and beyond.
Prof. Kulik will describe their efforts to accelerate the discovery of novel transition metal containing materials using machine learning. She will discuss how they have leveraged experimental data sets through both text mining and semantic embedding to uncover relationships between structure and function in molecular catalysts and metal-organic frameworks. Then she will describe how they have leveraged large datasets of synthesized materials to uncover those with novel function in polymer networks. She will describe how they demonstrate the success of their design strategy through macroscopically visible changes in network scale properties.
Decarbonizing transportation, the grid, and heavy industries depends on the success of both short- and long-duration energy storage solutions. Through novel material design and chemistry, my lab addresses critical challenges in developing affordable, sustainable, and reliable energy storage technologies. For short (to medium)-duration storage, we design and develop new cathode materials for sodium-ion batteries rich in manganese and iron. Our goal is to achieve energy densities comparable to lithium-ion batteries but at lower costs, without relying on critical minerals, thereby accelerating the transition to more sustainable energy storage. For long-duration storage, we have developed groundbreaking pathways for producing hydrogen (H₂) and ammonia (NH₃) using subsurface chemistry. By harnessing redox reactions on Fe-rich rocks and utilizing the Earth's natural heat and pressure, we demonstrate the potential for stimulated geological H₂ and NH₃ production. These methods achieve near-zero CO₂ emissions while remaining cost-competitive with existing technologies. Our work integrates advanced materials design with sustainable chemistry to provide scalable, impactful solutions for a decarbonized future.
Catarina Madeira Director, MIT Startup Exchange
Educating Manufacturing Technologists to be Future Shop Floor Leaders
John Liu Director & Principal Investigator, MIT Learning Engineering and Practice Group (LEAP)
Capitalizing on Change: Financing Manufacturing in the 21st Century
Hiram Samel Senior Lecturer, MIT Sloan School of Management
Manufacturing-Integrated Design from the Desktop to Deep-sea: novel robots and construction materials
Kaitlyn Becker Assistant Professor, MIT Mechanical Engineering Department