Publications de Seyedmohamad Moosavi

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

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

Reticular chemistry for the rational design of mechanically robust mesoporous merged-net metal-organic frameworks

Berend Smit, Seyedmohamad Moosavi

Access to metal-organic frameworks (MOFs) with enhanced me-chanical stability is key to their successful deployment in practical applications. However, the high porosity of the material often af-fects mechanical stability. In this article, to achieve highl ...

CELL PRESS2023

A Robust Framework for Generating Adsorption Isotherms to Screen Materials for Carbon Capture

Berend Smit, Elias Moubarak, Susana Garcia Lopez, Seyedmohamad Moosavi

To rank the performanceof materials for a given carbon captureprocess, we rely on pure component isotherms from which we predictthe mixture isotherms. For screening a large number of materials,we also increasingly rely on isotherms predicted from molecular ...

AMER CHEMICAL SOC2023

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

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

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

A data-science approach to predict the heat capacity of nanoporous materials

Berend Smit, Elias Moubarak, Susana Garcia Lopez, Balázs Álmos Novotny, Seyedmohamad Moosavi, Daniele Ongari, Mehrdad Asgari, Andres Adolfo Ortega Guerrero, Özge Kadioglu

The heat capacity of a material is a fundamental property of great practical importance. For example, in a carbon capture process, the heat required to regenerate a solid sorbent is directly related to the heat capacity of the material. However, for most m ...

NATURE PORTFOLIO2022

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

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

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

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

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

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

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

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 ...

NATURE RESEARCH2021

Toward smart carbon capture with machine learning

Berend Smit, Seyedmohamad Moosavi, Mohammad Rahimi

Machine learning (ML) is emerging as a powerful approach that has recently shown potential to affect various frontiers of carbon capture, a key interim technology to assist in the mitigation of climate change. In this perspective, we reveal how ML implemen ...

ELSEVIER2021