Publications de Kevin Maik Jablonka

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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 ...

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 ...

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, ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

Knowledge of the oxidation state of metal centres in compounds and materials helps in the understanding of their chemical bonding and properties. Chemists have developed theories to predict oxidation states based on electron-counting rules, but these can f ...

The design rules for materials are clear for applications with a single objective. For most applications, however, there are often multiple, sometimes competing objectives where there is no single best material and the design rules change to finding the se ...

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

Kevin Maik Jablonka

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

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

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

Making the collective knowledge of chemistry open and machine actionable

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

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

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

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

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

Leveraging large language models for predictive chemistry

SELFIES and the future of molecular string representations

Applicability of Tail Corrections in the Molecular Simulations of Porous Materials

A holistic platform for accelerating sorbent-based carbon capture

Nonuniform Chiralization of Metal-Organic Frameworks Using Imine Chemistry

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

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

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

Using collective knowledge to assign oxidation states of metal cations in metal-organic frameworks

Bias free multiobjective active learning for materials design and discovery

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