{"id":168704,"date":"2025-10-30T15:47:11","date_gmt":"2025-10-30T19:47:11","guid":{"rendered":"https:\/\/www.bu.edu\/eng\/?p=168704"},"modified":"2025-10-30T15:48:51","modified_gmt":"2025-10-30T19:48:51","slug":"accelerating-progress-with-self-driving-labs","status":"publish","type":"post","link":"https:\/\/www.bu.edu\/eng\/2025\/10\/30\/accelerating-progress-with-self-driving-labs\/","title":{"rendered":"Accelerating Progress with Self-Driving Labs"},"content":{"rendered":"

Tenth annual Materials Day focuses on automation in materials discovery<\/strong><\/h3>\n

Nearly 130 researchers gathered high on the 17th floor of Boston University\u2019s Duan Family Center for Computing & Data Sciences last week to share and learn about the successes and challenges of leveraging automation for materials discovery. The occasion was Materials Day 2025, <\/span>From Automation to Collaboration: The Future of Self-Driving Labs<\/span><\/i>.<\/span><\/p>\n

Sponsored by the Materials Science & Engineering (MSE<\/a>) division of the Boston University College of Engineering along with BU\u2019s Rafik B. Hariri Institute for Computing and Computational Science & Engineering, the day-long workshop featured nine faculty speakers from industry and institutions around the Boston area and beyond, as well as lightning talks by graduate students.<\/span><\/p>\n

\u201cMaterials research here at BU integrates approaches from many different domains, across engineering and beyond,\u201d said BU College of Engineering Dean Elise Morgan in opening remarks. \u201cWe think that convergent approach really helps address the complex challenges that aren\u2019t solvable by single-discipline thinking alone. And we focus on the problems that matter the most to people.\u201d<\/span><\/p>\n

The theme of this, the tenth annual Materials Day, \u201cfits perfectly with the college\u2019s mission and also the moment we are living in,\u201d Morgan added. \u201cThe potential of self-driving labs to democratize materials research and increase both the throughput and the sophistication of that research and its impact on society is clearly transformative at scale. And the emphasis of today\u2019s program on creating a community and banding together to accelerate progress is a wise tactic when threats to research funding abound.\u201d<\/span><\/p>\n

Bright ideas<\/b><\/h4>\n

A self-driving lab (SDL) is one in which robotic devices perform a great number of experiments consistently, aided by machine learning, toward a goal chosen by human scientists. A prime example is the lab of Keith Brown<\/a> (ME<\/a>, MSE, physics), who organized and emceed Materials Day with the help of administrators Elizabeth Flagg and Lea Sabra of MSE and Maureen Stanton of the Hariri Institute.<\/span><\/p>\n

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Keith Brown (ME, MSE, physics) and collaborator Emily Whiting (computer science). Photo by Charisma Nguyen-Lai<\/em><\/figcaption><\/figure>\n

Brown\u2019s <\/span>KABLab<\/span><\/a> used a system Brown calls the Bayesian experimental autonomous researcher (BEAR), combining additive manufacturing, robotics, and machine learning to do thousands of experiments until the team discovered the most efficient material ever for absorbing energy\u2014ideal for applications such as crash helmets or packaging. The lab has since developed the BEAR DEN, a collection of automated systems that enables them to make fundamental advances in polymers.<\/span><\/p>\n

Noting how Thomas Edison and his team at Menlo Park tested 6,000 materials before arriving at the filament that worked for the electric light bulb, Brown said, \u201cThe term \u2018Edisonian\u2019 has in some ways come to mean a brute-force approach that doesn\u2019t leave room for thought. And we should be thoughtful. But we also need to do a lot of experiments. Automation helps us in this process.\u201d<\/span><\/p>\n

In addition to the relatively simple robots physically carrying out experiments, advances in machine learning mean that software can actually choose the experiments, guided by researchers. \u201cThe idea that you can take computers and use them to solve complicated problems is a core part of the vision of self-driving labs,\u201d said Brown. \u201cLet\u2019s compute what we can to try to do better and smarter experiments.\u201d<\/span><\/p>\n

The Taco Bell model<\/b><\/h4>\n

For a scientist\u2014and indeed for the research community and society generally\u2014the most important benefit of an automated lab is its ability to faithfully replicate an experiment over and over and over again, said Professor Douglas Densmore<\/a> (ECE<\/a>, BME<\/a>, MSE).<\/span><\/p>\n

\u201cYou see robots, you see software\u2014it\u2019s all in the service of reproducibility,\u201d Densmore said. \u201cHigher throughput is great, lower cost is great, but it\u2019s really reproducibility. It\u2019s that if we were able to get this result, you could get the same result using a similarly configured lab. That\u2019s my metric.\u201d<\/span><\/p>\n

Densmore runs the Design, Automation, Manufacturing and Processes (DAMP) Lab. During the pandemic, the heavily automated lab processed up to 6,000 COVID-19 tests a day. Now, it\u2019s a resource for synthetic biology researchers seeking to create genetic materials. And, Densmore said, the lab\u2019s efficiency is not unlike that of a fast-food franchise.<\/span><\/p>\n

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Hariri Institute Director Yannis Paschalidis delivered remarks at Materials Day 2025. Photo by Charisma Nguyen-Lai<\/em><\/figcaption><\/figure>\n

\u201cPeople who work at Taco Bell go through a standardized training,\u201d Densmore said. \u201cThey know they won\u2019t be there forever. When someone leaves, Taco Bell doesn\u2019t say, \u2018Well, Joe left. We\u2019re done.\u2019 In our lab, we love our people, but when a student graduates or a postdoc leaves, we don\u2019t say, \u2018What are we gonna do?\u2019 We have standardized processes in place.\u201d<\/span><\/p>\n

Parameters for polymers<\/b><\/h4>\n

Assistant Professor Joerg Werner<\/a> (ME, MSE) spoke about how his research is well suited to SDLs. Werner studies polymer materials, which have applications in packaging, coatings, advanced electronics, batteries, and drug delivery.<\/span><\/p>\n

\u201cPolymers\u2019 properties depend on how you process the material,\u201d Werner said. \u201cThere are lots and lots of parameters that matter. So it\u2019s a perfect case for automation.\u201d<\/span><\/p>\n

For example, Werner, along with collaborators such as doctoral student Zhaoyi Zheng (who delivered one of the day\u2019s lightning talks), used Brown\u2019s BEAR DEN to speed up the development of the Electrodeposition of Polymer Networks mechanism, a novel method for fabricating polymer thin films on conductive materials of any shape.<\/span><\/p>\n

\u201cWe hope we can drive forward this kind of polymers materials discovery,\u201d said Werner, \u201cusing electrodeposition to make the films, modular chemistry to modify them, and closed-loop experimentation.\u201d<\/span><\/p>\n

A range of topics<\/b><\/h4>\n

Other speakers, hailing from universities such as MIT, Cornell, and Carnegie Mellon, addressed Bayesian optimization, the challenges that atomic systems pose to machine learning, and workforce development, to name a few aspects of SDLs.\u00a0<\/span><\/p>\n

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Steve Cranford, editor-in-chief of Cell Press journal <\/em>Matter. Photo by Charisma Nguyen-Lai<\/em><\/figcaption><\/figure>\n

For example, Nadya Peek of the University of Washington discussed how DIY and makerspace approaches can help lower the barriers to automation. Tonio Buonnassisi of MIT explored how automation can reduce bottlenecks in characterization and shared his lab\u2019s success in discovering novel perovskite-inspired materials. Malte Jung of Cornell sounded a cautionary note, pointing to research about how the automated lab environment might undermine teamwork among human researchers. (The full list of speakers is<\/span> here<\/span><\/a>.)<\/span><\/p>\n

The lightning talks by students were an important component of the program. “It’s nice to have the opportunity to share our recent focus with the whole community,” said Jiashuo Wang, a doctoral student in the KABLab. “A few people reached out to me afterwards to talk about more details or share their experiences and thoughts in similar areas, which is very helpful for our continuing research and collaboration.”<\/p>\n

The full picture<\/b><\/h4>\n
\"\"
The first community ideation exercise at Materials Day. Photo by Charisma Nguyen-Lai<\/em><\/figcaption><\/figure>\n

In a new feature of Materials Day, this year\u2019s attendees engaged in a community ideation exercise. Throughout the workshop, the researchers were encouraged to jot down open questions, grand challenges, and other relevant thoughts on sticky notes and, during breaks, to stick the notes on poster boards devoted to various topics: hardware, software, modular chemistry, human-machine learning, community, and convergence. After lunch, all participants stood up and circulated throughout the room, congregating at the poster boards, reading and commenting on each other\u2019s questions. The exercise sparked lively discussions and, perhaps, future collaborations.<\/span><\/p>\n

As Buonassisi said, live gatherings of this nature provide the scientific community with something not found in a textbook. \u201cYou need to hear from other people,\u201d he said. \u201cOur oral history, this way of sharing is the best we\u2019ve got right now. If you want the full picture, come to a workshop like this.\u201d<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"

Materials researchers shared successes and challenges with automation.<\/p>\n","protected":false},"author":2662,"featured_media":168719,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":[],"categories":[236,257,899,1092,1051,907,1041,1035,1104,1078,909,908],"tags":[775,983,1103,1279,671],"_links":{"self":[{"href":"https:\/\/www.bu.edu\/eng\/wp-json\/wp\/v2\/posts\/168704"}],"collection":[{"href":"https:\/\/www.bu.edu\/eng\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.bu.edu\/eng\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.bu.edu\/eng\/wp-json\/wp\/v2\/users\/2662"}],"replies":[{"embeddable":true,"href":"https:\/\/www.bu.edu\/eng\/wp-json\/wp\/v2\/comments?post=168704"}],"version-history":[{"count":11,"href":"https:\/\/www.bu.edu\/eng\/wp-json\/wp\/v2\/posts\/168704\/revisions"}],"predecessor-version":[{"id":168729,"href":"https:\/\/www.bu.edu\/eng\/wp-json\/wp\/v2\/posts\/168704\/revisions\/168729"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.bu.edu\/eng\/wp-json\/wp\/v2\/media\/168719"}],"wp:attachment":[{"href":"https:\/\/www.bu.edu\/eng\/wp-json\/wp\/v2\/media?parent=168704"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.bu.edu\/eng\/wp-json\/wp\/v2\/categories?post=168704"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.bu.edu\/eng\/wp-json\/wp\/v2\/tags?post=168704"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}