Publications de Kevin Maik Jablonka

Crossing the Valley of Death: A Holistic Approach to Assess the Potential of Sorbent-Based Carbon Capture Technologies

Seyedmohamad Moosavi, Berend Smit, Elias Moubarak, Kevin Maik Jablonka

Elsevier BV2025

Nonuniform Chiralization of Metal-Organic Frameworks Using Imine Chemistry

Emad Oveisi, Natalia Gasilova, Berend Smit, Elias Moubarak, Kevin Maik Jablonka, Andres Adolfo Ortega Guerrero, Balázs Álmos Novotny, Sauradeep Majumdar

Homochiral metal-organic frameworks (MOFs) are exceptional media for heterogeneous enantiodifferentiation processes. Modifying available achiral structure-bearing MOF scaffolds is a preferred method to extend this class of materials. Reported postsynthetic ...

AMER CHEMICAL SOC2025

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

Berend Smit, Elias Moubarak, Kevin Maik Jablonka, Yutao Li

MOFChecker, a package for MOF duplicate detection, geometric and charge error checking, and structure correction. ...

Royal Society of Chemistry (RSC)2025

Prioritizing Mentorship as Scientific Leaders

Kevin Maik Jablonka, Júlio César De Souza Terra

Amer Chemical Soc2024

Leveraging large language models for predictive chemistry

Philippe Schwaller, Berend Smit, Kevin Maik Jablonka, Andres Adolfo Ortega Guerrero

Machine learning has transformed many fields and has recently found applications in chemistry and materials science. The small datasets commonly found in chemistry sparked the development of sophisticated machine learning approaches that incorporate chemic ...

Nature Portfolio2024

A holistic platform for accelerating sorbent-based carbon capture

Seyedmohamad Moosavi, Berend Smit, Elias Moubarak, Kevin Maik Jablonka

Reducing carbon dioxide (CO2) emissions urgently requires the large-scale deployment of carbon-capture technologies. These technologies must separate CO2 from various sources and deliver it to different sinks1,2. The quest for optimal solutions for specifi ...

2024

Deep learning-based recommendation system for metal-organic frameworks (MOFs)

Berend Smit, Kevin Maik Jablonka, Xiaoqi Zhang

This work presents a recommendation system for metal-organic frameworks (MOFs) inspired by online content platforms. By leveraging the unsupervised Doc2Vec model trained on document-structured intrinsic MOF characteristics, the model embeds MOFs into a hig ...

Royal Soc Chemistry2024

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

Anne-Clémence Corminboeuf, Berend Smit, Kevin Maik Jablonka, Sauradeep Majumdar, Rubén Laplaza Solanas, Beatriz Bueno Mouriño

The current generation of large language models (LLMs) has limited chemical knowledge. Recently, it has been shown that these LLMs can learn and predict chemical properties through fine-tuning. Using natural language to train machine learning models opens ...

ROYAL SOC CHEMISTRY2024

14 examples of how LLMs can transform materials science and chemistry: a reflection on a large language model hackathon

Philippe Schwaller, Berend Smit, Kevin Maik Jablonka, Sauradeep Majumdar, Xiaoqi Zhang, Andres Camilo Marulanda Bran

Large-language models (LLMs) such as GPT-4 caught the interest of many scientists. Recent studies suggested that these models could be useful in chemistry and materials science. To explore these possibilities, we organized a hackathon. This article chronic ...

Royal Soc Chemistry2023

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

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

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 Tracki ...

WILEY-V C H VERLAG GMBH2023

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

Berend Smit, Kevin Maik Jablonka, Susana Garcia Lopez

Biomass is a highly versatile renewable resource for decarbonizing energy systems. Gasification is a promising conversion technology that can transform biomass into multiple energy carriers to produce heat, electricity, biofuels, or chemicals. At present, ...

Royal Soc Chemistry2023

Toward data-driven materials design: From atoms to pilot plants

Kevin Maik Jablonka

Discovering new materials is essential but challenging, time-consuming, and expensive. In many cases, simulations can be useful for estimating material properties. For many of the most interesting properties, however, simulations are infeasible because of ...

EPFL2023

Characterization of Chemisorbed Species and Active Adsorption Sites in Mg-Al Mixed Metal Oxides for High-Temperature CO2 Capture

Berend Smit, Christopher Patrick Ireland, Kevin Maik Jablonka, Susana Garcia Lopez

Mg-Al mixed metal oxides (MMOs), derived from the decomposition of layered double hydroxides (LDHs), have been purposed as adsorbents for CO2 capture of industrial plant emissions. To aid in the design and optimization of these materials for CO2 capture at ...

AMER CHEMICAL SOC2022

Making the collective knowledge of chemistry open and machine actionable

Luc Patiny, Berend Smit, Kevin Maik Jablonka

Large amounts of data are generated in chemistry labs-nearly all instruments record data in a digital form, yet a considerable proportion is also captured non-digitally and reported in ways non-accessible to both humans and their computational agents. Chem ...

NATURE PORTFOLIO2022

Data-Driven Matching of Experimental Crystal Structures and Gas Adsorption Isotherms of Metal-Organic Frameworks

Berend Smit, Daniele Ongari, Christoph Leopold Talirz, Kevin Maik Jablonka

Porous metal-organic frameworks are a class of materials with great promise in gas separation and gas storage applications. Due to the high dimensional space of materials science and engineering, computational screening techniques have long been an importa ...

AMER CHEMICAL SOC2022

SELFIES and the future of molecular string representations

Philippe Schwaller, Berend Smit, Kevin Maik Jablonka

Artificial intelligence (AI) and machine learning (ML) are expanding in popularity for broad applications to challenging tasks in chemistry and materials science. Examples include the prediction of properties, the discovery of new reaction pathways, or the ...

ELSEVIER2022

Using genetic algorithms to systematically improve the synthesis conditions of Al-PMOF

Fatmah Ebrahim, Seyedmohamad Moosavi, Berend Smit, Christopher Patrick Ireland, Christoph Leopold Talirz, Kevin Maik Jablonka

The synthesis of metal-organic frameworks (MOFs) is often complex and the desired structure is not always obtained. In this work, we report a methodology that uses a joint machine learning and experimental approach to optimize the synthesis conditions of A ...

NATURE PORTFOLIO2022

Making Molecules Vibrate: Interactive Web Environment for the Teaching of Infrared Spectroscopy

Luc Patiny, Berend Smit, Kevin Maik Jablonka

Infrared spectroscopy (IR) is a staple structural elucidation and characterization technique because of its ability to identify functional groups and its ease of use. Interestingly, it allows the capture of electronic effects via their influence on the bon ...

AMER CHEMICAL SOC2022

A data-driven perspective on the colours of metal-organic frameworks

Luc Patiny, Seyedmohamad Moosavi, Berend Smit, Mehrdad Asgari, Christopher Patrick Ireland, Kevin Maik Jablonka

Colour is at the core of chemistry and has been fascinating humans since ancient times. It is also a key descriptor of optoelectronic properties of materials and is often used to assess the success of a synthesis. However, predicting the colour of a materi ...

2021

Diversifying Databases of Metal Organic Frameworks for High-Throughput Computational Screening

Seyedmohamad Moosavi, Berend Smit, Daniele Ongari, Kevin Maik Jablonka, Sauradeep Majumdar

By combining metal nodes and organic linkers, an infinite number of metal organic frameworks (MOFs) can be designed in silico. Therefore, when making new databases of such hypothetical MOFs, we need to ensure that they not only contribute toward the growth ...

AMER CHEMICAL SOC2021