About

Michael P. Brenner is a Catalyst Professor of Applied Mathematics, Applied Physics, and Physics at Harvard University, affiliated with the Harvard John A. Paulson School of Engineering and Applied Sciences. He serves as the Area Chair for Applied Mathematics and is a Kavli Scholar at the Kavli Institute for Bionano Science & Technology. His research areas include applied mathematics, fluid mechanics, modeling physical and biological phenomena and systems, artificial intelligence, science and engineering for climate technology, applied physics, soft matter science, bioengineering, biomechanics, computer science, computational and data science, electrical engineering, environmental science and engineering, materials science, and mechanical engineering. Brenner's work involves applying computational frameworks, physics-based machine learning algorithms, and modeling techniques to biomolecular design, cellular organization, and other complex physical and biological systems, contributing to advancements in scientific research and engineering.

Research topics

• Computer Science
• Natural Language Processing
• Artificial Intelligence
• Machine Learning
• Psychology
• Communication
• Linguistics
• Econometrics
• Mathematics
• Speech recognition
• Economics
• Statistics
• Business
• Cognitive psychology
• Medicine
• Data science
• World Wide Web
• Geography
• Environmental health

Selected publications

• Expert evaluation of LLM world models: A high-T _<i>c</i> superconductivity case study

  Proceedings of the National Academy of Sciences · 2026-03-10

  articleOpen access

  Large Language Models (LLMs) show great promise as a powerful tool for scientific literature exploration. However, their effectiveness in providing scientifically accurate and comprehensive answers to complex questions within specialized domains remains an active area of research. Using the field of high-temperature cuprates as an exemplar, we evaluate the ability of LLM systems to understand the literature at the level of an expert. We construct an expert-curated database of 1,726 scientific papers that covers the history of the field, and a set of 67 expert-formulated questions that probe deep understanding of the literature. We then evaluate six different LLM-based systems for answering these questions, including both commercially available closed models and a custom retrieval-augmented generation (RAG) system capable of retrieving images alongside text. Experts then evaluate the answers of these systems against a rubric that assesses balanced perspectives, factual comprehensiveness, succinctness, and evidentiary support. Among the six systems, two using RAG on curated literature outperformed existing closed models across key metrics, particularly in providing comprehensive and well-supported answers. We discuss promising aspects of LLM performances as well as critical short-comings of all the models. The set of expert-formulated questions and the rubric will be valuable for assessing expert level performance of LLM based reasoning systems.

  PublisherOA PDFDOI

• Data for Non-Equilibrium Sensing of Volatile Compounds Using Active and Passive Analyte Delivery

  Zenodo (CERN European Organization for Nuclear Research) · 2026-05-26

  datasetOpen access

  Version 2: Added missing files to sniffing_data.zip See GitHub repository for data processing functions and examples: https://github.com/soerenbrandt/sniffing-sensor Abstract:Sensor technologies have allowed us to outperform the human senses of sight, hearing, and touch; however, the development of artificial noses is significantly behind their biological counterparts. This is largely due to the complexity of natural olfaction, as it incorporates complex fluid dynamics within the nasal anatomy together with the response patterns of hundreds to thousands of unique molecular-scale receptors for odor interpretation. We designed a sensing approach to identify volatiles that exploits time-dependent information from a single sensor (here, the reflectance spectra from a mesoporous one-dimensional photonic crystal) by augmenting and accentuating differences in the non-equilibrium mass-transport dynamics of vapors stemming from their distinct physicochemical properties, thus obviating the need for a large sensor array. By training a machine learning algorithm on the sensor output, we clearly identify polar and nonpolar volatile organic compounds, determine the mixing ratios of binary mixtures, and accurately predict the boiling point, flash point, vapor pressure, and viscosity of several volatile liquids within those used for training as well as compounds unknown to the model. We further implement a bioinspired active sniffing approach, in which the fluid dynamics and patterns of analyte delivery are controlled, enabling an additional modality of differentiation and reducing the duration of data collection and analysis to seconds. These results outline a strategy to build accurate and rapid artificial noses for volatile liquids that can provide useful information on chemicals such as their composition and properties, and can be applied in a variety of fields, including disease diagnosis, hazardous waste management, and healthy building monitoring.

  PublisherDOI

• Towards AI-assisted Academic Writing

  2025-01-01

  articleOpen accessSenior author

  Daniel J. Liebling, Malcolm Kane, Madeleine Grunde-McLaughlin, Ian Lang, Subhashini Venugopalan, Michael Brenner. Proceedings of the 1st Workshop on AI and Scientific Discovery: Directions and Opportunities. 2025.

  PublisherOA PDFDOI

• Quantum many-body physics calculations with large language models

  Communications Physics · 2025-01-31 · 8 citations

  articleOpen access

  Abstract Large language models (LLMs) have demonstrated abilities to perform complex tasks in multiple domains, including mathematical and scientific reasoning. We demonstrate that with carefully designed prompts, LLMs can accurately carry out key calculations in research papers in theoretical physics. We focus on a broadly-used approximation method in quantum physics: the Hartree-Fock method, requiring an analytic multi-step calculation deriving approximate Hamiltonian and corresponding self-consistency equations. To carry out the calculations using LLMs, we design multi-step prompt templates that break down the analytic calculation into standardized steps with placeholders for problem-specific information. We evaluate GPT-4’s performance in executing the calculation for 15 papers from the past decade, demonstrating that, with the correction of intermediate steps, it can correctly derive the final Hartree-Fock Hamiltonian in 13 cases. Aggregating across all research papers, we find an average score of 87.5 (out of 100) on the execution of individual calculation steps. We further use LLMs to mitigate the two primary bottlenecks in this evaluation process: (i) extracting information from papers to fill in templates and (ii) automatic scoring of the calculation steps, demonstrating good results in both cases.

  PublisherOA PDFDOI

• Coupling differential adhesion to cell signaling avoids kinetic traps to yield robust multicellular self-organization

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-10-24

  preprintOpen access

  Abstract Differential adhesion, where cells physically reorganize based on their heterogeneous adhesion preferences, is one of the major models for self-organization in development and tissue formation. However, accumulating evidence suggests that differential adhesion is many times insufficient for robust convergence to a target minimal energy multicellular structure. Here we use computational simulations and engineered synthetic cell circuits to systematically explore alternative mechanisms for programming formation of a simple two-cell type core-shell morphology. Starting with two pre-differentiated cell types with constitutively high differential adhesion leads to kinetic trapping in variable, multi-core structures. In contrast, hybrid mechanisms that gradually induce differential adhesion upon cell-cell contact signaling consistently converge to the target single-core structure, in a manner robust to variation in cell numbers, interaction energy, and noise. This work delineates intrinsic limitations of self-organizing systems based solely on differential adhesion, and shows how inducible systems provide a way to invoke the strong adhesion required to maintain a multicellular structure, while avoiding the pitfall of kinetic traps. This study illustrates how joint computational and experimental exploration of synthetic circuits can be used to probe key developmental principles and tradeoffs and inform the design of synthetic development and self-organization.

  PublisherOA PDFDOI

• Temporal structure of natural language processing in the human brain corresponds to layered hierarchy of large language models

  Nature Communications · 2025-11-26 · 3 citations

  articleOpen access

  Large Language Models (LLMs) offer a framework for understanding language processing in the human brain. Unlike traditional models, LLMs represent words and context through layered numerical embeddings. Here, we demonstrate that LLMs' layer hierarchy aligns with the temporal dynamics of language comprehension in the brain. Using electrocorticography (ECoG) data from participants listening to a 30-minute narrative, we show that deeper LLM layers correspond to later brain activity, particularly in Broca's area and other language-related regions. We extract contextual embeddings from GPT-2 XL and Llama-2 and use linear models to predict neural responses across time. Our results reveal a strong correlation between model depth and the brain's temporal receptive window during comprehension. We also compare LLM-based predictions with symbolic approaches, highlighting the advantages of deep learning models in capturing brain dynamics. We release our aligned neural and linguistic dataset as a public benchmark to test competing theories of language processing.

  PublisherOA PDFDOI

• Magnetic decoupling as a proofreading strategy for high-yield, time-efficient microscale self-assembly

  Proceedings of the National Academy of Sciences · 2025-08-28

  articleOpen access

  Life thrives due to its remarkable ability to create complex structures through the self-assembly of proteins, nucleic acids, and other biomolecules. Achieving such complex assemblies with the same level of fidelity, reproducibility, and advanced functionality in synthetic systems, however, has remained a grand challenge. One outstanding problem is the presence of parasitic products and long-lived intermediate states that slow the reaction process and limit the yield of the final product. Biology overcomes this challenge by proofreading to recognize and disassemble parasitic products. Such local checks, however, are currently difficult to implement in available self-assembly platforms. Here, we overcome this challenge by implementing a proofreading mechanism in a self-assembly platform. Specifically, we design intermediate states that strongly couple to an external force but a final product that is decoupled and thus highly stable to external driving, such that application of external forces selectively dissociates parasitic products. To implement this idea, we introduce lithographically patterned magnetic dipoles and an applied magnetic field to drive an assembly process similar to thermal self-assembly, but with additional controls. By applying patterns of magnetic driving that selectively destabilize parasitic states, we effectively implement a proofreading strategy to enable high-yield, time-efficient self-assembly. This realization of a general proofreading mechanism bridges the gap between artificial and biological self-assembly, paving the way for advanced self-assembled materials, with applications in next generation responsive materials, biomimetic devices, and microscale machines.

  PublisherDOI

• Generalized design of sequence–ensemble–function relationships for intrinsically disordered proteins

  Nature Computational Science · 2025-10-06 · 4 citations

  article
  PublisherDOI

• Hierarchical Self-Assembly of Magnetic Handshake Materials

  ACS Nano · 2025-04-11 · 2 citations

  article

  Through programmable self-assembly, simple building blocks can be made to form highly complex structures following local rules of interaction. However, materials systems that are most commonly utilized for programmable assembly often lack interactions that exhibit the strength, specificity, and long ranges, which would, as a result, allow for robust and rapid hierarchical self-assembly processes. "Magnetic handshake" building blocks resolve many of these challenges at once, incorporating strong, long-range, and specific magnetic interactions through patterning of magnetic dipoles onto rigid panels. When appropriately designed, the panels organize hierarchically: first into chains, and subsequently those chains combine to form dense stacks. Here, we examine differences in phase behavior and morphology for four panel types. We delineate how perpendicular chaining and stacking interactions between panels compete and how they can be manipulated to reverse the sequence of the hierarchical assembly pathway. Collectively, our work shows the enormous potential for using magnetic handshake materials for self-assembly of hierarchically organized complex structures.

  PublisherDOI

• FEABench: Evaluating Language Models on Multiphysics Reasoning Ability

  ArXiv.org · 2025-04-08

  preprintOpen access

  Building precise simulations of the real world and invoking numerical solvers to answer quantitative problems is an essential requirement in engineering and science. We present FEABench, a benchmark to evaluate the ability of large language models (LLMs) and LLM agents to simulate and solve physics, mathematics and engineering problems using finite element analysis (FEA). We introduce a comprehensive evaluation scheme to investigate the ability of LLMs to solve these problems end-to-end by reasoning over natural language problem descriptions and operating COMSOL Multiphysics$^\circledR$, an FEA software, to compute the answers. We additionally design a language model agent equipped with the ability to interact with the software through its Application Programming Interface (API), examine its outputs and use tools to improve its solutions over multiple iterations. Our best performing strategy generates executable API calls 88% of the time. LLMs that can successfully interact with and operate FEA software to solve problems such as those in our benchmark would push the frontiers of automation in engineering. Acquiring this capability would augment LLMs' reasoning skills with the precision of numerical solvers and advance the development of autonomous systems that can tackle complex problems in the real world. The code is available at https://github.com/google/feabench

  PublisherOA PDFDOI

Recent grants

• DMREF: Self Assembly with DNA-Labeled Colloidal Particles and DNA Nanostructures

  NSF · $1.5M · 2014–2019

• Research and Education in Physical Mathematics

  NSF · $288k · 2006–2010

• Research and Education in Physical Mathematics

  NSF · $399k · 2014–2019

• Research and Education in Physical Mathematics

  NSF · $546k · 2009–2013

• NIH Grant P50GM068763

  NIH · $24.4M · 2014

Frequent coauthors

• Carl P. Goodrich

  Institute of Science and Technology Austria

  35 shared

• David A. Weitz

  Harvard University

  32 shared

• Alain Pumir

  École Normale Supérieure de Lyon

  31 shared

• Detlef Lohse

  Max Planck University of Twente Center for Complex Fluid Dynamics

  28 shared

• Lucy J. Colwell

  University of Cambridge

  27 shared

• Vinothan Manoharan

  Harvard University

  25 shared

• Bobbi Aubrey

  Princeton University

  23 shared

• Ofer Kimchi

  Princeton University

  22 shared

Labs

• Michael P. Brenner LabPI