IEM Seminar Series: Fast-Tracking Sustainability: Accelerating Innovation with Computation and Automation
Abstract
As climate change and global competition intensify, so does the pressure to innovate faster. In response, researchers are increasingly combining computational tools for rapid decision-making with robotics that interact directly with the physical world. These approaches span from partially automated workflows to fully autonomous, self-driving laboratories. While the promise of accelerated discovery is attracting growing interest and investment, realizing meaningful progress demands a strategic, long-term perspective — one that integrates infrastructure, research, and education in a coordinated way. This will be the central focus of my talk, viewed through the lens of sustainability.
I’ll begin by reviewing select efforts over the past decade to accelerate research, development, and deployment (RD&D), tracing their evolution from proof-of-concept “toy problems” to real-world examples of sustainable-materials innovation achieved on compressed timelines. Next, I’ll highlight three challenging areas where I believe strategic investment could yield especially high returns: predicting the synthesizability of new materials, improving the stability of promising candidates, and scaling up for manufacturing. Along the way, I’ll share some modest successes from our own work, including the discovery of new perovskite-inspired materials and the optimization of existing ones. Finally, I’ll address the role of education in preparing the workforce for an AI-enabled future, and embracing finance as an enabler for successful innovation.
Bio
Prof. Tonio Buonassisi (Mechanical Engineering, MIT) works at the intersection of machine learning (ML), automation, and materials science to speed up the discovery and deployment of technologies with broad societal impact. His early work in solar energy and technoeconomic analysis supported dozens of companies and earned him the Presidential Early Career Award for Scientists and Engineers (PECASE). From 2018 to 2021, he served as founding director of the Accelerated Materials Development for Manufacturing (AMDM) program in Singapore — a S$24.7M initiative that demonstrated more than a 10× acceleration in materials development by integrating ML, automation, and simulation. Now back at MIT full-time, he leads the Accelerated Materials Laboratory for Sustainability. Since 2023 he directs the ADDEPT Center, a U.S. Department of Energy-funded effort that unites academic and industry partners to develop more durable, efficient, and reproducible perovskite-based tandem photovoltaic modules.
As climate change and global competition intensify, so does the pressure to innovate faster. In response, researchers are increasingly combining computational tools for rapid decision-making with robotics that interact directly with the physical world. These approaches span from partially automated workflows to fully autonomous, self-driving laboratories. While the promise of accelerated discovery is attracting growing interest and investment, realizing meaningful progress demands a strategic, long-term perspective — one that integrates infrastructure, research, and education in a coordinated way. This will be the central focus of my talk, viewed through the lens of sustainability.
I’ll begin by reviewing select efforts over the past decade to accelerate research, development, and deployment (RD&D), tracing their evolution from proof-of-concept “toy problems” to real-world examples of sustainable-materials innovation achieved on compressed timelines. Next, I’ll highlight three challenging areas where I believe strategic investment could yield especially high returns: predicting the synthesizability of new materials, improving the stability of promising candidates, and scaling up for manufacturing. Along the way, I’ll share some modest successes from our own work, including the discovery of new perovskite-inspired materials and the optimization of existing ones. Finally, I’ll address the role of education in preparing the workforce for an AI-enabled future, and embracing finance as an enabler for successful innovation.
Bio
Prof. Tonio Buonassisi (Mechanical Engineering, MIT) works at the intersection of machine learning (ML), automation, and materials science to speed up the discovery and deployment of technologies with broad societal impact. His early work in solar energy and technoeconomic analysis supported dozens of companies and earned him the Presidential Early Career Award for Scientists and Engineers (PECASE). From 2018 to 2021, he served as founding director of the Accelerated Materials Development for Manufacturing (AMDM) program in Singapore — a S$24.7M initiative that demonstrated more than a 10× acceleration in materials development by integrating ML, automation, and simulation. Now back at MIT full-time, he leads the Accelerated Materials Laboratory for Sustainability. Since 2023 he directs the ADDEPT Center, a U.S. Department of Energy-funded effort that unites academic and industry partners to develop more durable, efficient, and reproducible perovskite-based tandem photovoltaic modules.
Practical information
- General public
- Free
Contact
- Jean-PhilippeThiran