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Kevin Maik Jablonka

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Related publications (27)

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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

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

Berend Smit, Luc Patiny, 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

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

Berend Smit, Kevin Maik Jablonka

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

Making the collective knowledge of chemistry open and machine actionable

Berend Smit, Luc Patiny, 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

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

Berend Smit, Anne-Clémence Corminboeuf, Rubén Laplaza Solanas, Kevin Maik Jablonka, Sauradeep Majumdar, Joren Jozef M. Van Herck, Beatriz Bueno Mouriño, Seyed Amirmohammad Elahi

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

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

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

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

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

Big-Data Science in Porous Materials: Materials Genomics and Machine Learning

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

By combining metal nodes with organic linkers we can potentially synthesize millions of possible metal–organic frameworks (MOFs). The fact that we have so many materials opens many exciting avenues but also create new challenges. We simply have too many ma ...

2020

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

Berend Smit, Philippe Schwaller, Kevin Maik Jablonka, Sauradeep Majumdar, Joren Jozef M. Van Herck, 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

Charge Separation and Charge Carrier Mobility in Photocatalytic Metal‐Organic Frameworks

Berend Smit, Ivano Tavernelli, Kevin Maik Jablonka, Maria Fumanal Quintana

Metal‐organic frameworks (MOFs) are highly versatile materials owing to their vast structural and chemical tunability. These hybrid inorganic–organic crystalline materials offer an ideal platform to incorporate light‐harvesting and catalytic centers and th ...

2020

Leveraging large language models for predictive chemistry

Berend Smit, Philippe Schwaller, 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

SELFIES and the future of molecular string representations

Berend Smit, Philippe Schwaller, 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

Applicability of Tail Corrections in the Molecular Simulations of Porous Materials

Berend Smit, Kevin Maik Jablonka, Daniele Ongari

Molecular simulations with periodic boundary conditions require the definition of a certain cutoff radius, r(c) beyond which pairwise dispersion interactions are neglected. For the simulation of homogeneous phases the use of tail corrections is well-establ ...

2019

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

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

A holistic platform for accelerating sorbent-based carbon capture

Berend Smit, Seyedmohamad Moosavi, Kevin Maik Jablonka, Elias Moubarak, Joren Jozef M. Van Herck

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

Nonuniform Chiralization of Metal-Organic Frameworks Using Imine Chemistry

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

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

Understanding the diversity of the metal-organic framework ecosystem

Berend Smit, Seyedmohamad Moosavi, Daniele Ongari, Kevin Maik Jablonka, Peter George Boyd, Yongjin Lee

Millions of distinct metal-organic frameworks (MOFs) can be made by combining metal nodes and organic linkers. At present, over 90,000 MOFs have been synthesized and over 500,000 predicted. This raises the question whether a new experimental or predicted s ...

Nature Research2020

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

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

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

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

Berend Smit, Kevin Maik Jablonka, Christopher Patrick Ireland

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