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Berend Smit

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Professor

École Polytechnique Fédérale de Lausanne

Switzerland

Research Interests

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Professors in Switzerland Professors in Chemistry Chemistry professors in Switzerland

About

Professor Berend Smit is a faculty member at the Ecole Polytechnique Federale de Lausanne in Switzerland. His research primarily focuses on material science and chemical engineering, particularly in the areas of metal-organic frameworks (MOFs), direct air capture, and photocatalysis. He has published extensively on the challenges of sorbent discovery and the application of large language models in chemistry. His recent work includes investigations into anisotropic distortions in MOFs and the validation of MOF structures through innovative computational tools.

Recent Grants

Grant: Open

Big Data in Nanoporous Materials: Science beyond Understanding

Open Date: 2023-12-01

Close Date: 2028-11-30

Grant: Open

Strategies to find the optimal sorbent material and process conditions for direct air capture

Open Date: 2023-11-01

Close Date: 2027-10-31

Grant: Close

The Materials Genome in Action

Open Date: 2015-11-01

Close Date: 2021-04-30

Articles (33)

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Collaborators (40)

Jiale Shi

Assistant Professor

University of Toledo

UNITED STATES

Berend Smit

Research Interests

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About

Recent Grants

Articles (33)

Peer Review and AI: Your (Human) Opinion Is What Matters

The data-only illusion in materials discovery

Pore Geometry–Driven Capture of Trace Aromatic Volatile Organic Compounds in Al-Based MOFs

Hydration-dependent exciton-phonon coupling in a metal-organic framework photocatalyst

Tunable Microporous Bimetallic Carboxylate-Pyrazolate Metal–Organic Frameworks for CO <sub>2</sub> Capture

Generalized Machine Learning Potentials for Predicting Low-Pressure Water Adsorption in Flexible Al-Based Metal–Organic Frameworks

Machine learning potential for modelling dynamic hydrogen bond networks in MOF MIL-120

Nonuniform Chiralization of Metal–Organic Frameworks Using Imine Chemistry

Exploring the chemical design space of metal–organic frameworks for photocatalysis

Assessment of fine-tuned large language models for real-world chemistry and material science applications

Correspondence on “The Open DAC 2023 Dataset and Challenges for Sorbent Discovery in Direct Air Capture”

Correspondence on "The Open DAC 2023 Dataset and Challenges for Sorbent Discovery in Direct Air Capture"

Anisotropic node distortions in amorphous MOFs: Low-valent Zr sites as catalytic hotspots

Anisotropic node distortions in amorphous MOFs: Low-valent Zr sites as catalytic hotspots

Assessment of fine-tuned large language models for real-world chemistry and material science applications

Exploring the potential landscape of chemical engineering science

Exploring the potential landscape of chemical engineering science

MOFChecker: a package for validating and correcting metal–organic framework (MOF) structures

Nonuniform Chiralization of Metal-Organic Frameworks Using Imine Chemistry

Lead, Locked Away: Porous Zr–Phytate Coordination Polymers for Rapid and Selective Removal of Pb2+ from Water

Thermal transport mechanisms in ZIFs

FFLAME: a fragment-to-framework learning approach for MOF potentials

Unraveling metal effects on CO<sub>2</sub> uptake in pyrene-based metal-organic frameworks

In Search of Covalent Organic Framework Photocatalysts: A DFT‐Based Screening Approach

Tandem Hydroformylation‐Aldol Condensation Reaction Enabled by Zn‐MOF‐74

A Robust Framework for Generating Adsorption Isotherms to Screen Materials for Carbon Capture

Toward Superior Hydroisomerization Catalysts through Thermodynamic Optimization

An Ecosystem for Digital Reticular Chemistry

A multi-modal pre-training transformer for universal transfer learning in metal–organic frameworks

How to Decarbonize Our Energy Systems: Process‐Informed Design of New Materials for Carbon Capture

Machine learning for industrial processes: Forecasting amine emissions from a carbon capture plant

14 Examples of How LLMs Can Transform Materials Science and Chemistry: A Reflection on a Large Language Model Hackathon

Biomass to energy: a machine learning model for optimum gasification pathways

Collaborators (40)

Jiale Shi

Aurélie Champagne

Andrew Rosen

Kevin Maik Jablonka

Marco Ranocchiari

Tanja Junkers

Sara E. Skrabalak

Christian Serre

Teri Odom

Kazunori Kataoka

Jillian Mary Buriak

María Victoria Gil

Wendy Queen

Ben Blaiszik

Georges Mouchaham

Katherine Willets

Andy Sode Anker

Elena Simone

Bettina Baumgartner

Michael Köpke

Amir Mohammad Elahi

Yang Chai

Xiaodong Chen

Antonios Alvertis

Michael Jewett

Seyed Mohamad Moosavi

Hyunsoo Park

Mark C. Hersam

Joshua Bocarsly

Eranda Nikolla

Balázs Álmos Novotny

Yeonghun Kang

Deji Akinwande

Il-Doo Kim

Susana Garcia

Yayuan Liu

Erick Carreira

Mohammad Khaja Nazeeruddin

Xiaoqi Zhang