Berend Smit

A multi-modal pre-training transformer for universal transfer learning in metal-organic frameworks

Metal-organic frameworks (MOFs) are a class of crystalline porous materials that exhibit a vast chemical space owing to their tunable molecular building blocks with diverse topologies. An unlimited number of MOFs can, in principle, be synthesized. Machine learning approaches can help to explore this vast chemical space by identifying optimal candidates with desired properties from structure-property relationships. Here we introduce MOFTransformer, a multi-modal Transformer encoder pre-trained with 1 million hypothetical MOFs. This multi-modal model utilizes integrated atom-based graph and energy-grid embeddings to capture both local and global features of MOFs, respectively. By fine-tuning the pre-trained model with small datasets ranging from 5,000 to 20,000 MOFs, our model achieves state-of-the-art results for predicting across various properties including gas adsorption, diffusion, electronic properties, and even text-mined data. Beyond its universal transfer learning capabilities, MOFTransformer generates chemical insights by analyzing feature importance through attention scores within the self-attention layers. As such, this model can serve as a platform for other MOF researchers that seek to develop new machine learning models for their work. Metal-organic frameworks are of high interest for a range of energy and environmental applications due to their stable gas storage properties. A new machine learning approach based on a pre-trained multi-modal transformer can be fine-tuned with small datasets to predict structure-property relationships and design new metal-organic frameworks for a range of specific tasks.

NATURE PORTFOLIO2023

How to Decarbonize Our Energy Systems: Process-Informed Design of New Materials for Carbon Capture

Berend Smit

Decarbonisation from a variety of industrial and power emission sectors highlights a marked need for capture technologies that can be optimized for different CO2 sources and integrated into an equally diverse range of applications of captured CO2 as a feedstock. Some capture technologies are already operated at an industrial scale but may not be optimal for all required applications. Advanced tailored sorbent-based technologies allow flexible operation and reduced costs as they offer higher capture capacities and significantly lower energy penalties than the state-of-the-art systems. To accelerate the discovery, development, and deployment of novel advanced materials, it is critically important that efforts between experimentalists, theoreticians, and process engineers are coordinated. The PrISMa project addresses this challenge by integrating materials design with process design and environmental considerations to allow for tailor-making carbon capture solutions optimally tuned for local sources and sinks. In this article, we highlight some of the recent results obtained with the PrISMa platform.

WILEY-V C H VERLAG GMBH2023

Tandem Hydroformylation-Aldol Condensation Reaction Enabled by Zn-MOF-74

Daniele Ongari, Berend Smit

The tandem hydroformylation-aldol condensation (tandem HF-AC) reaction offers an efficient synthetic route to the synthesis of industrially relevant products. The addition of Zn-MOF-74 to the cobalt-catalyzed hydroformylation of 1-hexene enables tandem HF-AC under milder pressure and temperature conditions than the aldox process, where zinc salts are added to cobalt-catalyzed hydroformylation reactions to promote aldol condensation. The yield of the aldol condensation products increases by up to 17 times compared to that of the homogeneous reaction without MOF and up to 5 times compared to the aldox catalytic system. Both Co-2(CO)(8) and Zn-MOF-74 are required to significantly enhance the activity of the catalytic system. Density functional theory simulations and Fourier-transform infrared experiments show that heptanal, the product of hydroformylation, adsorbs on the open metal site (OMS) of Zn-MOF-74, thereby increasing the electrophilic character of the carbonyl carbon atom and facilitating the condensation.

WILEY-V C H VERLAG GMBH2023

Toward Superior Hydroisomerization Catalysts through Thermodynamic Optimization

Berend Smit

The need to reduce the lifecycle greenhouse gas emissions of fuels and lubricants has renewed interest in hydroisomerization processes. Here it is shown how recognizing the signature of individual alkane hydrocracking pathways enables delineating changes to the alkane hydroisomerization and hydrocracking networks as a function of catalyst pore topology. Spacious pores allow indiscriminate access to both kinetically favored alpha,alpha,gamma-trialkyl alkane hydrocracking pathways and thermodynamically favored alpha,alpha- and alpha,gamma-dialkyl alkane hydrocracking pathways. Narrower pores enhance isomerization by slowing down access to the kinetically favored hydrocracking pathways. The narrowest pores enhance isomerization further by narrowing down the remaining hydrocracking pathways: MTT-and TON-type zeolites limit hydrocracking to only a low concentration of the few isomers that are commensurate with their respective topologies. This improved understanding of how catalyst pore topologies impact hydroconversion networks is guiding the design of more carbon efficient industrial hydroisomerization processes.

AMER CHEMICAL SOC2023

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

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

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 materials suitable for carbon capture applications, the heat capacity is not known, and thus the standard procedure is to assume the same value for all materials. In this work, we developed a machine learning approach, trained on density functional theory simulations, to accurately predict the heat capacity of these materials, that is, zeolites, metal-organic frameworks and covalent-organic frameworks. The accuracy of our prediction is confirmed with experimental data. Finally, for a temperature swing adsorption process that captures carbon from the flue gas of a coal-fired power plant, we show that for some materials, the heat requirement is reduced by as much as a factor of two using the correct heat capacity.

NATURE PORTFOLIO2022

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

Christopher Patrick Ireland, Kevin Maik Jablonka, Berend Smit

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 200 degrees C, we have used a combination of solid-state nuclear magnetic resonance (ssNMR) and density functional theory (DFT) to characterize the CO2 gas sorption products and determine the various sorption sites in Mg-Al MMOs. A comparison of the DFT cluster calculations with the observed C-13 chemical shifts of the chemisorbed products indicates that mono- and bidentate carbonates are formed at the Mg-O sites with adjacent Al substitution of an Mg atom, while the bicarbonates are formed at Mg-OH sites without adjacent Al substitution. Quantitative C-13 NMR shows an increase in the relative amount of strongly basic sites, where the monodentate carbonate product is formed, with increasing Al/Mg molar ratios in the MMOs. This detailed understanding of the various basic Mg-O sites presented in MMOs and the formation of the carbonate, bidentate carbonate, and bicarbonate chemisorbed species yields new insights into the mechanism of CO2 adsorption at 200 degrees C, which can further aid in the design and capture capacity optimization of the materials.

AMER CHEMICAL SOC2022

Effects of Degrees of Freedom on Calculating Diffusion Properties in Nanoporous Materials br

Raffaela Cabriolu, Berend Smit, Henglu Xu

If one carries out a molecular simulation ofNparticlesusing periodic boundary conditions, linear momentum is conserved,and hence, the number of degrees of freedom is set to 3N-3. Inmost programs, this number of degrees of freedom is the defaultsetting. However, if one carries out a molecular simulation in anexternalfield, one needs to ensure that degrees of freedom arechanged from this default setting to 3N, as in an externalfield thevelocity of the center of mass can change. Using the correct degrees offreedom is important in calculating the temperature and in somealgorithms to simulate at constant temperature. For sufficiently largesystems, the difference between 3Nand 3N-3 is negligible.However, there are systems in which the comparison withexperimental data requires molecular dynamics simulations of asmall number of particles. In this work, we illustrate the effect of an incorrect setting of degrees of freedom in molecular dynamicsimulations studying the diffusion properties of guest molecules in nanoporous materials. We show that previously published resultshave reported a surprising diffusion dependence on the loading, which could be traced back to an incorrect setting of the degrees offreedom. As the correct settings are convoluted and counterintuitive in some of the most commonly used molecular dynamicsprograms, we carried out a systematic study on the consequences of the various commonly used (incorrect) settings. Our conclusionis that for systems smaller than 50 particles the results are most likely unreliable as these are either performed at an incorrecttemperature or the temperature is incorrectly used in some of the results. Furthermore, a novel and efficient method to calculatediffusion coefficients of guest molecules into nanoporous materials at zero-loading conditions is introduced

AMER CHEMICAL SOC2022

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

Kevin Maik Jablonka, Luc Patiny, Berend Smit

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 bond strength of "probes", such as the carbonyl group and also offers a wealth of examples for discussion on the theory of electronic transitions. For this reason, IR spectroscopy is typically taught both in theoretical classes and in applied structural analysis courses. In practice, there is rarely a link between those courses, and both suffer from the lack of exploratory learning, that is, tools with which students can explore the interplay between symmetry and selection rules, as well as electronic effects and vibrational frequencies-with almost immediate feedback. In practice, this might lead to students that are well skilled in looking up vibrational frequencies in lookup tables but do not understand the links to electronic effects and reactivity. Here, we introduce a web app that leverages semiempirical quantum mechanical (or force-field based) calculations, performed on a web service, in an interactive interface to provide an environment in which students can explore how slight changes to the structure manifest in changes of the spectrum. This approach avoids the time-consuming handling of potentially hazardous materials that might not be readily available and invites students to play with spectroscopy-to "see" and "test" electronic effects that are so commonplace in organic chemistry education. As a "side effect", our web app also provides a powerful aid for research scientists to investigate how different structural modifications, such as substitution, isomerism, or steric strain, would manifest in the infrared spectrum.

AMER CHEMICAL SOC2022

Making the collective knowledge of chemistry open and machine actionable

Kevin Maik Jablonka, Luc Patiny, Berend Smit

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. Chemical research is still largely centred around paper-based lab notebooks, and the publication of data is often more an afterthought than an integral part of the process. Here we argue that a modular open-science platform for chemistry would be beneficial not only for data-mining studies but also, well beyond that, for the entire chemistry community. Much progress has been made over the past few years in developing technologies such as electronic lab notebooks that aim to address data-management concerns. This will help make chemical data reusable, however it is only one step. We highlight the importance of centring open-science initiatives around open, machine-actionable data and emphasize that most of the required technologies already exist-we only need to connect, polish and embrace them.

NATURE PORTFOLIO2022