PeopleofSuperintelligence

Researcher

Jake Smith

Also known as Jake A. Smith

Jake Smith is a chemist and senior researcher at Microsoft Research who uses AI to speed up the development of new materials. He studied chemistry at Rice University, then earned a doctorate in chemistry with the Moeller Group at Washington University and coauthored a 2017 review on anodic cyclization reactions. At Microsoft, his first project was DNA data storage, including a 2021 Science Advances paper on the first nanoscale DNA storage writer. His AI work spans the MOFDiff diffusion model in 2023, the MatterGen generative model for inorganic materials published in Nature in 2025, and CLIO, a reasoning agent that worked with chemists in 2026 to design a molecule for a flow battery. He is part of the Microsoft Climate Research Initiative in Redmond.

Timeline

  1. 2015–2017

    Chemistry at Rice and WashU

    He earns a B.S. in chemistry at Rice University and a Ph.D. in chemistry at Washington University in St. Louis with the Moeller Group. With the group, he coauthors a 2015 Beilstein Journal of Organic Chemistry paper on photovoltaic-driven organic electrosynthesis and a 2017 Accounts of Chemical Research review on anodic cyclization reactions with Ruozhu Feng and Kevin D. Moeller.

  2. 2021–2024

    DNA data storage at Microsoft

    After postdoctoral work on modifying proteins and on small-molecule drug design, he joins Microsoft to work with Karin Strauss on making DNA at large scale for digital data storage. In November 2021, he coauthors a Science Advances paper on the first nanoscale DNA storage writer. For a September 2024 Nature Communications study of particle radiation damage in DNA data storage, he performs the kinetic modeling.

  3. October 2023

    MOFDiff for carbon capture

    MOFDiff, which he coauthors with Xiang Fu, Tian Xie, Andrew S. Rosen and Tommi Jaakkola in October 2023, is a coarse-grained diffusion model that generates metal-organic framework structures and is tested on designing materials for carbon capture. The paper appears at ICLR 2024.

  4. December 2023–January 2025

    The MatterGen generative model

    In December 2023, Microsoft Research AI4Science announces MatterGen, a generative model for property-guided materials design, and thanks him among its contributors. The Nature paper, published in January 2025, lists him at Microsoft Research AI for Science in Redmond; MatterGen generates stable, diverse inorganic materials across the periodic table and can be fine-tuned toward a broad range of property constraints.

  5. 2024

    Recyclable circuit boards

    He coauthors an April 2024 Nature Sustainability paper on recyclable vitrimer-based printed circuit boards, led by University of Washington researchers and funded in part by the Microsoft Climate Research Initiative; he is an affiliate researcher in UW's Allen School. On the Microsoft Research Podcast in July 2024, he and Aniruddh Vashisth discuss using machine learning to design sustainable polymers. An Advanced Science paper he coauthors in December 2024 applies molecular dynamics and generative modeling to the inverse design of vitrimeric polymers.

  6. October 2025

    SimPoly force fields for polymers

    SimPoly, posted as a preprint in October 2025 with him among the coauthors, is a machine learning force field that predicts polymer densities and glass transition temperatures from first principles. The work also introduces a benchmark of experimental bulk properties for 130 polymers and a quantum-chemical dataset.

  7. May 2026

    The CLIO design agent

    In May 2026, he coauthors a preprint on CLIO (Cognitive Loop via In-Situ Optimization), a reasoning agent that leads proposal and interpretation in a closed-loop campaign with chemists to design an aqueous organic redox flow battery negolyte. Across 17 candidates over three rounds, the final sulfonate compound keeps a 90 mV improvement in redox potential over the literature baseline.

  8. As of October 2026

    Senior researcher at Microsoft

    As of October 2026, he is a senior researcher at Microsoft Research in Redmond and part of the Microsoft Climate Research Initiative. His work at Microsoft spans DNA data storage, sustainability and the use of AI to speed up the development of new materials.

  1. Google had the most people shaping AI every year from 2014 to 2025Our estimates of which organizations employed the most people shaping AI from 1961 to 2025, with a year-by-year chart.
  2. The United States employs an estimated 53% of people in our data with a known employment countryOur estimates of where AI careers are concentrated now and how countries’ shares of dated positions changed over time.
  3. 46% of living people in our data with known birth and employment countries work outside their birth countryOur estimate of cross-border careers in AI, from country of birth and the country of each current organization.
  4. More people shaping AI studied at Massachusetts Institute of Technology than at any other institutionOur estimates of where the people shaping AI studied and where their careers led, from education records in cited biographies.

Sources and credits

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  1. Jake Smith at Microsoft Research microsoft.com. Checked 19 October 2026.
  2. Publications | The Moeller Research Group | Washington University in St. Louis sites.wustl.edu. Checked 19 October 2026.
  3. Collaborators: Sustainable electronics with Jake Smith and Aniruddh Vashisth - Microsoft Research microsoft.com. Checked 19 October 2026.
  4. Scaling DNA data storage with nanoscale electrode wells - Microsoft Research microsoft.com. Checked 19 October 2026.
  5. Evaluating the risk of data loss due to particle radiation damage in a DNA data storage system | Nature Communications nature.com. Checked 19 October 2026.
  6. [2310.10732] MOFDiff: Coarse-grained Diffusion for Metal-Organic Framework Design arxiv.org. Checked 19 October 2026.
  7. Jake Smith at Microsoft Research microsoft.com. Checked 19 October 2026.
  8. MatterGen: Property-guided materials design - Microsoft Research microsoft.com. Checked 19 October 2026.
  9. A generative model for inorganic materials design | Nature nature.com. Checked 19 October 2026.
  10. New circuit boards can be repeatedly recycled | UW News washington.edu. Checked 19 October 2026.
  11. [2510.13696] SimPoly: Simulation of Polymers with Machine Learning Force Fields Derived from First Principles arxiv.org. Checked 19 October 2026.
  12. [2606.02618] Closed-Loop Molecular Design with Calibrated Deference arxiv.org. Checked 19 October 2026.