Keith Brown

About

Keith Brown is an Associate Professor of Mechanical Engineering, Materials Science & Engineering, and Physics at Boston University. He earned an S.B. in Physics from MIT and a Ph.D. in Applied Physics from Harvard University, where he worked with Robert M. Westervelt. He was also an International Institute for Nanotechnology postdoctoral fellow with Chad A. Mirkin at Northwestern University. His research group, the KABlab, focuses on accelerating the development of advanced materials and structures, particularly polymers, through approaches such as self-driving labs, miniaturization of experiments using scanning probe techniques, novel platforms for parallel materials development, and machine learning. Keith Brown has co-authored over 90 peer-reviewed publications and holds six issued patents. His work has been recognized with awards including the Frontiers of Materials Award from The Minerals, Metals, & Materials Society (TMS), recognition as a “Future Star of the AVS,” the Omar Farha Award for Research Leadership from Northwestern University, and the AVS Nanometer-Scale Science and Technology Division Postdoctoral Award. He has served on the Nano Letters Early Career Advisory Board and currently leads the MRS Artificial Intelligence in Materials Development Staging Task Force. His research interests include nanomanufacturing and hierarchical materials, with a focus on merging top-down patterning and bottom-up assembly, investigating how mesoscopic order influences the…

Research topics

• Computer Science
• Artificial Intelligence
• Machine Learning
• Physics
• Materials science
• Mathematics
• Nanotechnology
• Political Science
• Optics
• Cell biology

Selected publications

• Autonomous experimentation systems for materials development: A community perspective

  Matter · 2021 · 344 citations

  DOI

• A Bayesian experimental autonomous researcher for mechanical design

  Science Advances · 2020 · 307 citations

  Senior authorCorresponding

  While additive manufacturing (AM) has facilitated the production of complex structures, it has also highlighted the immense challenge inherent in identifying the optimum AM structure for a given application. Numerical methods are important tools for optimization, but experiment remains the gold standard for studying nonlinear, but critical, mechanical properties such as toughness. To address the vastness of AM design space and the need for experiment, we develop a Bayesian experimental autonomou…

  DOI

• Benchmarking the performance of Bayesian optimization across multiple experimental materials science domains

  npj Computational Materials · 2021 · 194 citations

  Abstract Bayesian optimization (BO) has been leveraged for guiding autonomous and high-throughput experiments in materials science. However, few have evaluated the efficiency of BO across a broad range of experimental materials domains. In this work, we quantify the performance of BO with a collection of surrogate model and acquisition function pairs across five diverse experimental materials systems. By defining acceleration and enhancement metrics for materials optimization objectives, we find…

  DOI

• Using simulation to accelerate autonomous experimentation: A case study using mechanics

  iScience · 2021 · 62 citations

  Senior authorCorresponding

  Autonomous experimentation (AE) accelerates research by combining automation and machine learning to perform experiments intelligently and rapidly in a sequential fashion. While AE systems are most needed to study properties that cannot be predicted analytically or computationally, even imperfect predictions can in principle be useful. Here, we investigate whether imperfect data from simulation can accelerate AE using a case study on the mechanics of additively manufactured structures. Initially…

  DOI

• Science acceleration and accessibility with self-driving labs

  Nature Communications · 2025-04-24 · 55 citations

  reviewOpen access

  In the evolving landscape of scientific research, the complexity of global challenges demands innovative approaches to experimental planning and execution. Self-Driving Laboratories (SDLs) automate experimental tasks in chemical and materials sciences and the design and selection of experiments to optimize research processes and reduce material usage. This perspective explores improving access to SDLs via centralized facilities and distributed networks. We discuss the technical and collaborative…

  PublisherOA PDFDOI

Recent grants

• Collaborative Research: DMREF: Closed-Loop Design of Polymers with Adaptive Networks for Extreme Mechanics

  NSF · $400k · 2023–2027

• Tip-based Nanochemistry for Printing Soft Materials

  NSF · $479k · 2017–2022

Frequent coauthors

• Chad A. Mirkin

  Northwestern University

  86 shared

• Daniel J. Eichelsdoerfer

  23 shared

• R. M. Westervelt

  21 shared

• Matthew N. O’Brien

  17 shared

• Kelsey L. Snapp

  Boston University

  16 shared

• Aldair E. Gongora

  Lawrence Livermore National Laboratory

  16 shared

• Abigail Rendos

  Boston University

  15 shared

• David Issadore

  University of Pennsylvania

  14 shared

Labs

• KABlabPI

Education

• Ph.D., Physics

  University of California, Berkeley

  1990

• M.S., Physics

  University of California, Berkeley

  1986

• B.S., Physics

  University of California, Berkeley

  1984

Awards & honors

• Frontiers of Materials Award from The Minerals, Metals, & Ma…
• Future Star of the AVS
• Omar Farha Award for Research Leadership from Northwestern U…
• AVS Nanometer-Scale Science and Technology Division Postdoct…

Similar researchers at Boston University

• Ed Kearnsh-122
• Robert Careyh-110
• Lawrence R. Sulakh-107