Symposium X—MRS/The Kavli Foundation Frontiers of Materials
April 26, 2024
Tonio Buonassisi, Massachusetts Institute of Technology
Finding a Path from “Promise” to “Performance”—Toward Realizing Novel Materials Predicted via Generative AI
Written by Sophia Chen
It’s all the rage: artificial intelligence—from generative models to automated laboratories—will help researchers find novel and useful materials faster. But Tonio Buonassisi, a mechanical engineer at Massachusetts Institute of Technology, knows that to get to that place, the field has its work cut out for it.
“This is not your normal tech-vangelist talk where I'm going to go up and show you how great AI is and how autonomous labs are going to rock the world,” said Buonassisi during Thursday’s Symposium X. “Yes, I do believe that. And I also believe that the path to getting there is a hard one.”
In Thursday’s Symposium X, titled “Finding a Path from ‘Promise’ to ‘Performance’—Toward Realizing Novel Materials Predicted via Generative AI,” Buonassisi outlined his perspectives on the state of automation and automated materials discovery, as well as the role of AI in these processes.
Buonassisi focused on pragmatic challenges. The adoption of AI will involve growing pains, he says. He described the trajectory as curve, where the field will hit what feels like unproductive bottlenecks: “You're putting in a lot of investment and not getting out much return. So this talk is about a lot of that part of the curve, and creating community around getting us over that part.”
Currently, the materials community has already adopted automation in a variety of settings. Scientists at the University of Liverpool, for example, use co-bots, or robots designed to work in close proximity to humans. Researchers at Boston University have also developed a robot arm that coordinates with 3D printers to manipulate materials samples.
Working at Singapore’s Agency for Science, Technology, and Research, Buonassisi started a program known as Accelerated Materials Development for Manufacturing, where they adopted a method known as “islands of automation.” To do this, instead of aiming to create a fully autonomous materials discovery process, they focused on accelerating “specific parts of the process and […] strategically targeted those points in the workflow that require more time, or that suffer from irreproducibility due to human factors,” he said.
The adoption of AI, which largely comprises models known as neural network, presents many challenges for the field, Buonassisi described in the talk. For example, the field needs to better identify which problems are most appropriate for autonomous or automated systems. On top of that, they need to increase the odds that experiments can actually synthesize and test an AI-predicted material. He listed several practical questions in the use of AI: for example, how should researchers choose a neural network architecture? How do they select, obtain, and validate training data? How should researchers represent materials to the neural network? How do they diagnose issues when things are going wrong? How should they select candidate materials that the neural network generates? How should they quantify the uniqueness or the novelty of the AI-generated materials? “These are […] examples of some of the things that, in my opinion, would yield truly high-impact papers, if you're a graduate student, postdoc, or early-stage career professor,” said Buonassisi. Using AI to discover unusual materials presents a particular challenge, he said. Experts have concluded that they would probably not have been able to predict the cuprates, high-temperature superconductors, given today’s AI tools and data prior to 1987 when researchers discovered the material.
The field should also critically examine the social structures that drive materials research, Buonassisi said. Individuals face different incentives than those of academia as an institution, and that can present a tension. While Buonassisi says this is a systemic problem and does not promise any solutions, he proposes a different academic structure in which the total number of students and postdocs are reduced. As they become more senior, they begin to manage “undergraduate researchers and staff who can augment their capabilities, who can complement some of their weaknesses in computation automation, in domain expertise,” he said.
Symposium X—MRS/The Kavli Foundation Frontiers of Materials features lectures aimed at a broad audience to provide meeting attendees with an overview of leading-edge topics.