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Ekin Cubuk

Co-founder; formerly materials research lead, Google DeepMind, Periodic Labs

Search every verified Ekin Cubuk interview, podcast appearance, and on-the-record quote — each transcript cross-checked by AI and human review to confirm speaker identity. Ekin Cubuk, co-founder of Periodic Labs and formerly materials research lead at Google DeepMind, appeared on a podcast episode published October 7, 2026, to discuss the company's approach to AI-driven scientific discovery. He argued that even advanced models like GPT-7 would still need to run physical experiments to achieve meaningful results, stating that machine learning excels at trained tasks but scientific discovery involves what has not been trained on. He described Periodic Labs' strategy of building autonomous laboratories where models can interact with the real world, emphasizing that major discoveries require experimentation rather than relying solely on existing data. Cubuk also discussed the company's use of reinforcement learning environments derived directly from their physical labs, with data from those labs serving as ground truth. He contrasted this approach with optimization based on known answers from papers or textbooks, noting that Periodic Labs aims to go beyond established knowledge. He highlighted challenges such as decision-making under uncertainty in materials science, and referenced examples like magnesium diboride to illustrate how discoveries have historically come from trial and error rather than direct theoretical prediction.

Recent appearances

  • AI Scientists Are Here: Autonomous Labs & Synthesis Superintelligence — Periodic Labs

    October 07, 2026 · 📺 Latent Space

    From building AI systems that reason over noisy physical experiments to creating laboratories where every instrument can become intelligent, Periodic Labs is betting that the next frontier of AI won’t come from simply training on more internet data — it will come from letting models experiment with the real world. In this episode, Periodic Labs’ Liam Fedus and Ekin Dogus Cubuk join swyx and Brandon to explain why scientific discovery is fundamentally different from math and coding, and what it takes to build AI scientists that can actually discover new materials. We go deep on Periodic’s visi…

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  • Ekin Dogus Cubuk - Combining Experiments, LLMs, and Theory to Discover Quantum Materials

    August 09, 2026 · 📺 Berkeley RDI

    My name is Dogus Cubuk. I'm a co-CEO and cofounder of Periodic Labs. And we combine simulations, experiments, and LLMs to ...

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  • Fireside Chat with Ekin Dogus Cubuk, Co-CEO of Periodic Labs | RAISE Summit 2026

    July 20, 2026 · 📺 RAISE Summit

    Ekin Dogus Cubuk, Co-CEO of Periodic Labs, joins Forbes' Iain Martin to discuss using AI to accelerate physical science — and what happens when frontier models start running real experiments. Recorded live on the Master Stage at the Carrousel du Louvre, Paris, on July 9, 2026. Livestream powered by Cursor. RAISE Summit is Europe's leading AI gathering, bringing together 9,000+ founders, executives, investors, researchers and policymakers. Learn more: https://www.raisesummit.com Subscribe for every session from RAISE Summit 2026.

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  • Periodic Labs: Training AI Scientists, with Liam Fedus & Ekin Dogus Cubuk (from a16z)

    October 02, 2025 · 📺 Cognitive Revolution "How AI Changes Everything"

    Today’s special crosspost features ‪@a16z‬ General Partner Anjney Midha with Liam Fedus and Ekin Dogus Cubuk of Periodic Labs. They discuss experiments in training frontier AI models on real-world physics lab experiments—rather than digital reward functions—and how coupling language models with automated materials synthesis could accelerate the discovery of room-temperature superconductors and transform R&D in advanced manufacturing. Shownotes brought to you by Notion AI Meeting Notes - try one month for free at: https://notion.com/lp/nathan -Core innovation: Periodic Labs is creating a "phy…

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  • Materials Project Seminars – Ekin Dogus Cubuk - Scaling up material discovery via deep learning

    December 15, 2023 · 📺 MaterialsProject

    December 7, 2022 at 10:00AM (USA/Pacific) The work presented in this MP Seminar was published in Nature 1 year after this recording. https://nature.com/articles/s41586-02... Corresponding blog post https://deepmind.google/discover/blog... Talk Abstract: Materials science data and computational resources are growing rapidly. While machine learning offers a promising set of tools for learning models for large volumes of data (IID), these tools are not inherently good at generalizing to new (OOD) data. For this reason, the impact of deep learning on the computational discovery of stable inorga…

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The summary and quote tags on this profile are produced with AI assistance from verified, first-person interview transcripts, then checked by our team to confirm the speaker's identity and the accuracy of every quote. See how we verify →