Publications by Kevin Maik Jablonka

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

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

Elsevier BV2025

Nonuniform Chiralization of Metal-Organic Frameworks Using Imine Chemistry

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

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, Yutao Li, Elias Moubarak, Kevin Maik Jablonka

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

Júlio César De Souza Terra, Kevin Maik Jablonka

Amer Chemical Soc2024

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

A holistic platform for accelerating sorbent-based carbon capture

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

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

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

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

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

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