Randall Q. Snurr

About

Randall Q. Snurr is the John G. Searle Professor of Chemical and Biological Engineering at Northwestern University. His research is focused on developing new nanoporous materials to address critical issues related to energy and sustainability. He has made significant contributions in the development of materials for hydrogen storage for cleaner vehicles, CO2 capture, energy-efficient adsorption separations, and atmospheric water harvesting. His work also includes research on capturing pollutants such as PFAS from water and advancing catalysis through improved solid acid catalysts, selective oxidation, and the destruction of chemical warfare agents by hydrolysis. Snurr's primary tools in research include ab initio calculations, molecular simulations, multiscale modeling, and machine learning. His group has developed open-source software and publicly available databases used globally. Much of his work centers on metal-organic framework (MOF) materials, which are synthesized from metal nodes and organic linkers, allowing for modular chemistry and property tuning. He has pioneered computational methods to rapidly identify promising MOF materials among millions of possibilities, including generating and screening thousands of MOFs computationally. His approach has been validated through experimental synthesis and testing, demonstrating the effectiveness of computational screening in materials discovery. Snurr has received numerous recognitions, including fellowships, awards, and…

Research topics

• Computer Science
• Materials science
• Nanotechnology
• Artificial Intelligence
• Physics
• Chemistry
• Thermodynamics
• Organic chemistry
• Physical chemistry
• Machine Learning

Selected publications

• Machine learning the quantum-chemical properties of metal–organic frameworks for accelerated materials discovery

  Matter · 2021 · 475 citations

  Senior authorCorresponding

  The modular nature of metal–organic frameworks (MOFs) enables synthetic control over their physical and chemical properties, but it can be difficult to know which MOFs would be optimal for a given application. High-throughput computational screening and machine learning are promising routes to efficiently navigate the vast chemical space of MOFs but have rarely been used for the prediction of properties that need to be calculated by quantum mechanical methods. Here in this paper, we introduce th…

  DOI

• Inverse design of nanoporous crystalline reticular materials with deep generative models

  Nature Machine Intelligence · 2021 · 371 citations

  DOI

• Diffusion in Nanoporous Materials

  Diffusion fundamentals. · 2022 · 185 citations

  DOI

• Nanoconfinement and mass transport in metal–organic frameworks

  Chemical Society Reviews · 2021 · 165 citations

  The ubiquity of metal-organic frameworks in recent scientific literature underscores their highly versatile nature. MOFs have been developed for use in a wide array of applications, including: sensors, catalysis, separations, drug delivery, and electrochemical processes. Often overlooked in the discussion of MOF-based materials is the mass transport of guest molecules within the pores and channels. Given the wide distribution of pore sizes, linker functionalization, and crystal sizes, molecular…

  PublisherOA PDFDOI

• Connecting theory and simulation with experiment for the study of diffusion in nanoporous solids

  Adsorption · 2021 · 142 citations

  Abstract Nanoporous solids are ubiquitous in chemical, energy, and environmental processes, where controlled transport of molecules through the pores plays a crucial role. They are used as sorbents, chromatographic or membrane materials for separations, and as catalysts and catalyst supports. Defined as materials where confinement effects lead to substantial deviations from bulk diffusion, nanoporous materials include crystalline microporous zeotypes and metal–organic frameworks (MOFs), and a nu…

  DOI

Recent grants

• Collaborative Research: DMREF: GOALI: Discovering Materials for CO2 Capture in the Presence of Water via Integrated Experiment, Modeling, and Theory

  NSF · $1.4M · 2021–2025

• DMREF: Simulation-Driven Design of Highly Efficient MOF/Nanoparticle Hybrid Catalyst Materials

  NSF · $1.2M · 2013–2017

• NIRT: Design of Nanoporous Materials for Enantioselective Single-Site Catalysis and Separations

  NSF · $1.0M · 2005–2010

Frequent coauthors

• Omar K. Farha

  Northwestern University

  210 shared

• Joseph T. Hupp

  144 shared

• Krista S. Walton

  Georgia Institute of Technology

  69 shared

• Timur İslamoğlu

  Northwestern University

  69 shared

• David Dubbeldam

  University of Amsterdam

  69 shared

• Omar M. Yaghi

  King Abdulaziz City for Science and Technology

  61 shared

• Andrew Rosen

  58 shared

• Haoyuan Chen

  Nanjing Tech University

  57 shared

Awards & honors

• Paul Emmett and Richard Kokes Lecture, Department of Chemica…
• Fellow of the International Adsorption Society, 2020
• IChemE Senior Moulton Medal, 2020
• Corresponding Member of the Saxon Academy of Sciences and Hu…
• Ernest W. Thiele Award from the Chicago Local Section of AIC…

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