Milad Abolhasani

Chemical engineering has long been about optimization, scalability, and sustainability. By embedding those same principles into the architecture of SDLs, we can build a future of accelerated, reproducible, and democratized discovery.

This strategy aligns resource efficiency with discovery speed and empowers AI agents with richer, more reliable data streams.

Rather than retrofitting conventional reactors and instruments into autonomous workflows, we advocate for a bottom-up SDL design that treats every unit operation not just as a chemical transformation step, but also as a data-generating process module.

In this article, we argue that engineering principles, especially Process Intensification (PI), must serve as the foundation for SDL design. Why? Because PI enables '𝐃𝐚𝐭𝐚 𝐈𝐧𝐭𝐞𝐧𝐬𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧', that is the key to unlocking the full decision-making power of AI agents in SDLs.

As self-driving laboratories (SDLs) continue to gain momentum in chemistry and materials research, it’s critical to ask: Are we just automating yesterday’s tools, or are we rethinking experimentation from the ground up?

Excited to share our latest article published in 𝐍𝐚𝐭𝐮𝐫𝐞 𝐂𝐡𝐞𝐦𝐢𝐜𝐚𝐥 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠:

“𝘌𝘯𝘨𝘪𝘯𝘦𝘦𝘳𝘪𝘯𝘨 𝘗𝘳𝘪𝘯𝘤𝘪𝘱𝘭𝘦𝘴 𝘧𝘰𝘳 𝘚𝘦𝘭𝘧-𝘋𝘳𝘪𝘷𝘪𝘯𝘨 𝘓𝘢𝘣𝘰𝘳𝘢𝘵𝘰𝘳𝘪𝘦𝘴”
www.nature.com/articles/s44...

Please welcome @milad-a.bsky.social as our newest Associate Editor!
Milad is a prof. at NC State working on self-driving labs + automated discovery. From AIChE 35 Under 35 to a Dreyfus Award, he’s a force in the field—and we’re lucky to have him on board.
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