Berend Smit | EPFL Graph Search

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Berend Smit received an MSc in Chemical Engineering in 1987 and an MSc in Physics both from the Technical University in Delft (the Netherlands). He received in 1990 cum laude PhD in Chemistry from Utrecht University (the Netherlands). He was a (senior) Research Physicists at Shell Research from 1988-1997, Professor of Computational Chemistry at the University of Amsterdam (the Netherlands) 1997-2007.

In 2004 Berend Smit was elected Director of the European Center of Atomic and Molecular Computations (CECAM) Lyon France. Since 2007 he is Professor of Chemical Engineering and Chemistry at U.C. Berkeley and Faculty Chemist at Materials Sciences Division, Lawrence Berkeley National Laboratory. Since 2014 he has been director of the Energy Center at EPFL.

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CH-359: Project of Computational chemistry

Ce cours exploite les techniques de chimie quantique et de modélisation moléculaire dans le cadre d'un projet de recherche visant a résoudre un problème chimique en binôme.

CH-420: Understanding advanced molecular simulation

This course introduces advanced molecular simulation techniques such as Monte Carlo and Molecular dynamics in different ensembles, free energy calculations, rare events, Configurational-bias Monte Carlo etc.

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

Berend Smit

Metal-organic frameworks (MOFs) are a class of crystalline porous materials that exhibit a vast chemical space owing to their tunable molecular building blocks with diverse topologies. An unlimited number of MOFs can, in principle, be synthesized. Machine learning approaches can help to explore this vast chemical space by identifying optimal candidates with desired properties from structure-property relationships. Here we introduce MOFTransformer, a multi-modal Transformer encoder pre-trained with 1 million hypothetical MOFs. This multi-modal model utilizes integrated atom-based graph and energy-grid embeddings to capture both local and global features of MOFs, respectively. By fine-tuning the pre-trained model with small datasets ranging from 5,000 to 20,000 MOFs, our model achieves state-of-the-art results for predicting across various properties including gas adsorption, diffusion, electronic properties, and even text-mined data. Beyond its universal transfer learning capabilities, MOFTransformer generates chemical insights by analyzing feature importance through attention scores within the self-attention layers. As such, this model can serve as a platform for other MOF researchers that seek to develop new machine learning models for their work. Metal-organic frameworks are of high interest for a range of energy and environmental applications due to their stable gas storage properties. A new machine learning approach based on a pre-trained multi-modal transformer can be fine-tuned with small datasets to predict structure-property relationships and design new metal-organic frameworks for a range of specific tasks.

NATURE PORTFOLIO2023

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

Berend Smit

Decarbonisation from a variety of industrial and power emission sectors highlights a marked need for capture technologies that can be optimized for different CO2 sources and integrated into an equally diverse range of applications of captured CO2 as a feedstock. Some capture technologies are already operated at an industrial scale but may not be optimal for all required applications. Advanced tailored sorbent-based technologies allow flexible operation and reduced costs as they offer higher capture capacities and significantly lower energy penalties than the state-of-the-art systems. To accelerate the discovery, development, and deployment of novel advanced materials, it is critically important that efforts between experimentalists, theoreticians, and process engineers are coordinated. The PrISMa project addresses this challenge by integrating materials design with process design and environmental considerations to allow for tailor-making carbon capture solutions optimally tuned for local sources and sinks. In this article, we highlight some of the recent results obtained with the PrISMa platform.

WILEY-V C H VERLAG GMBH2023

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

Kevin Maik Jablonka, Beatriz Mouriño, Andres Adolfo Ortega Guerrero, Berend Smit

Covalent organic frameworks (COFs) stand out as prospective organic-based photocatalysts given their intriguing optoelectronic properties, such as visible light absorption and high charge-carrier mobility. The "Clean, Uniform, Refined with Automatic Tracking from Experimental Database" (CURATED) COFs is a database of reported experimental COFs that until now remained mostly unexplored for photocatalysis. In this study, the CURATED COFs database is screened for discovering potential photocatalysts using a set of DFT-based descriptors that cost-effectively assesses visible light absorption, preliminary thermodynamic feasibility of the desired pair of redox reactions, charge separation, and charge-carrier mobility. The workflow can shortlist 13 COFs as prospective candidates for water splitting, and identify materials (N-x-COF (x = 0-3)) that have been reported as candidates for hydrogen evolution reaction. Overall, the strategy addresses the challenge of exploring a large number of COFs by directing future research toward a selective group of COFs, while providing valuable insights into the structural design for achieving a desired photocatalytic process.

WILEY-V C H VERLAG GMBH2023