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Bonds and local atomic environments are crucial descriptors of material properties. They have been used to create design rules and heuristics and as features in machine learning of materials properties. Implementations and algorithms (e.g., ChemEnv and LobsterEnv) for identifying local atomic environments based on geometrical characteristics and quantum-chemical bonding analysis are nowadays available. Fully automatic workflows and analysis tools have been developed to use quantum-chemical bonding analysis on a large scale. The lecture will demonstrate how our tools, that assess local atomic environments and perform automatic bonding analysis, help to develop new machine learning models and a new intuitive understanding of materials. Furthermore, the general trend toward automation in density functional-based materials science and some of our recent contributions will be discussed.
Zirconium chloro fluoride as catalyst for C-F bond activation and HF transfer of fluoroalkanes
(2024)
In this work1, we have successfully synthesised amorphous zirconium chloro fluoride (ZCF), which exhibits medium lewis acidity. In addition to investigating the local coordination sphere around the Zr atoms and the material properties, we were able to establish a catalytic behavior of ZCF in C-F bond activation reactions. We present the first heterogeneous catalyst that performs dehydrofluorination of a fluoroalkane and consecutive hydrofluorination of an alkyne at room temperature.
The LOBSTER (Deringer et al., 2011;Maintz et al., 2013 ,2016 ;Nelson et al., 2020 ) software aids in extracting quantum-chemical bonding information from materials by projecting the plane-wave based wave functions from density functional theory (DFT) onto an atomic orbital basis. LobsterEnv, a module implemented in pymatgen (Ong et al., 2013) by some of the authors of this package, facilitates the use of quantum-chemical bonding information obtained from LOBSTER calculations to identify neighbors and coordination environments. LobsterPy is a Python package that offers a set of convenient tools to further analyze and summarize the LobsterEnv outputs in the form of JSONs that are easy to interpret and process. These tools enable the estimation of (anti) bonding contributions, generation of textual descriptions, and visualization of LOBSTER computation results. Since its first release, both LobsterPy and LobsterEnv capabilities have been extended significantly. Unlike earlier versions, which could only automatically analyze Crystal Orbital Hamilton Populations (COHPs) (Dronskowski & Blöchl, 1993), both can now also analyze Crystal Orbital Overlap Populations (COOP) (Hughbanks & Hoffmann, 1983) and Crystal Orbital Bond Index (COBI) (Müller et al., 2021). Extracting the information about the most important orbitals contributing to the bonds is optional, and users can enable it as needed. Additionally, bonding-based features for machinelearning (ML) studies can be engineered via the sub-packages “featurize” and “structuregraphs”. Alongside its Python interface, it also provides an easy-to-use command line interface (CLI) that runs automatic analysis of the computations and generates a summary of results and publication-ready figures. LobsterPy has been used to produce the results in Ngo et al. (2023), Chen et al. (2024), Naik et al. (2023), and it is also part of Atomate2 (2023) bonding analysis workflow for generating bonding analysis data in a format compatible with the Materials Project (Jain et al., 2013) API.
Bonds and local atomic environments are crucial descriptors of material properties. They have been used to create design rules and heuristics and as features in machine learning of materials properties. Implementations and algorithms (e.g., ChemEnv and LobsterEnv) for identifying local atomic environments based on geometrical characteristics and quantum-chemical bonding analysis are nowadays available. Fully automatic workflows and analysis tools have been developed to use quantum-chemical bonding analysis on a large scale. The lecture will demonstrate how our tools, that assess local atomic environments and perform automatic bonding analysis, help to develop new machine learning models and a new intuitive understanding of materials.[5,6] Furthermore, the general trend toward automation in density functional-based materials science and some of our recent contributions will be discussed.
Crystalline materials that exhibit reversible mechanical responses upon exposure to external stimuli have garnered significant attention owing to their potential applications in various fields. Herein, we report a crystal of (2Z,2′Z)-2,2′-(1,4-phenylene)bis(3-(4-bromophenyl)acrylonitrile) (DSBr), which displays simultaneous elasto-plastic behaviour and reversible thermosalient effects. While elasto-plastic behaviour is attributed to underlying packing features, reversible thermosalient expansion is attributed to uniaxial expansion mediated by heat. Exceptional length increase and contraction upon cooling is due to the restorative nature of weak interactions through a cooperative effect. The cooperative movement of molecules is reflected in the unidirectional expansion of the habit plane. Thermosalient reversible expansion–contraction in elasto-plastic crystals have not been discussed in the literature so far. Detailed analysis reported herein provides a comprehensive understanding of the underlying mechanism of flexibility and thermosalient responses. This crystal's unique blend of reversible thermal expansion with flexibility holds substantial promise for applications in flexible thermal actuators.
This study investigates the mechanochemical reactions between AgBr 3-picolylamine and 4-picolylamine. The use of different stoichiometry ratios of the reagents allows [(AgBr)(n-pica)]n and [(AgBr)2(n-pica)]n to be obtained, and we report the new structures of [(AgBr)2(3-pica)]n and [(AgBr)2(4-pica)]n which are characterized by the presence of the following: (a) infinite inorganic chains, (b) silver atom coordinated only by bromide atoms and (c) argentophilic interactions. Furthermore, we studied the interconversion of [(AgBr)(n-pica)]n/[(AgBr)2(n-pica)]n by mechanochemical and thermal properties. The in situ experiments suggest that [(AgBr)(3-pica)]n is kinetically favoured while [(AgBr)2(3-pica)]n is converted into [(AgBr)(3-pica)]n only with a high excess of the ligand. Finally, the liquid nature of the ligands is not sufficient to assist the grinding process, and the complete reaction is observed with the addition of a small quantity of acetonitrile.
Jobflow is a free, open-source library for writing and executing workflows. Complex workflows can be defined using simple python functions and executed locally or on arbitrary computing resources using the FireWorks workflow manager.
Some features that distinguish jobflow are dynamic workflows, easy compositing and connecting of workflows, and the ability to store workflow outputs across multiple databases.
We demonstrate the determination of anharmonic acoustic phonon properties via second-order Raman scattering exemplarily on copper iodide single crystals. The origin of multi-phonon features from the second-order Raman spectra was assigned by the support of the calculated 2-phonon density of states. In this way, the temperature dependence of acoustic phonons was determined down to 10 K. To determine independently the harmonic contributions of respective acoustic phonons, density functional theory in quasi-harmonic approximation was used. Finally, the anharmonic contributions were determined. The results are in agreement with earlier publications and extend CuI’s determined acoustic phonon properties to lower temperatures with higher accuracy. This approach demonstrates that it is possible to characterize the acoustic anharmonicities via Raman scattering down to zero-temperature renormalization constants of at least 0.1 cm−1.
We present Jobflow, a domain-agnostic Python package for writing computational workflows tailored for high-throughput computing applications. With its simple decorator-based approach, functions and class methods can be transformed into compute jobs that can be stitched together into complex workflows. Jobflow fully supports dynamic workflows where the full acyclic graph of compute jobs is not known until runtime, such as compute jobs that launch
other jobs based on the results of previous steps in the workflow. The results of all Jobflow compute jobs can be easily stored in a variety of filesystem- and cloud-based databases without the data storage process being part of the underlying workflow logic itself. Jobflow has been intentionally designed to be fully independent of the choice of workflow manager used to dispatch the calculations on remote computing resources. At the time of writing, Jobflow
workflows can be executed either locally or across distributed compute environments via an adapter to the FireWorks package, and Jobflow fully supports the integration of additional workflow execution adapters in the future.
This talk explores the intricate connections between scientists, focusing on the networking dynamics within the realm of metal-organic frameworks (MOFs). The study delves into the collaborative networks formed among scientists, shedding light on the synergistic relationships that contribute to advancements in MOF research.
Mechanochemistry has become a compelling method for producing (new) molecule s and mate-rials, but the inner workings of the milling jars remain a fascinating mystery. Advances in this field include tailor-made chemical systems and real-time revelations using techniques such as XRD and Raman spectroscopy.
This talk will discuss our recent progress in using X-ray diffraction and sophisticated spectros-copy to observe reactions in various material systems during ball milling and extrusion in real-time.
The complexity of mechanochemical reactions spans multiple scales and requires a holistic ap-proach. The categorisation of reactions by investigative methods precedes the exploration of real-time analysis that reveals macroscopic processes using synchrotron techniques.
During this exploration, one resounding realisation remains: We are on the threshold of under-standing. The complexity of mechanochemistry requires a collective effort, drawing on the ex-pertise of a diverse community. As we unravel the web of mechanochemical phenomena, we acknowledge the collaborative nature of this ongoing journey.
Mechanochemistry emerges as a potent, environmentally friendly, and straightforward approach for crafting novel multicomponent crystal systems. Various milling parameters, including milling frequency, time, filling degree of the milling jar, ball diameter, vessel size, degree of milling ball filling, and material of jars, are recognized influencers on the mechanisms and rates of product formation. Despite the growing interest in mechanochemistry, there exists a gap in understanding the mechanistic aspects of mechanochemical reactivity and selectivity. To address this, diverse analytical methods and their combinations, such as powder X-ray diffraction, X-ray absorption spectroscopy, NMR, Raman spectroscopy, and thermography, have been developed for real-time, in situ monitoring of mechanochemical transformations.
This discussion centers on our recent findings, specifically investigating the formation of (polymorphic) cocrystals and metal-organic frameworks. Through these studies, we aim to unravel the impact of milling parameters and reaction sequences on the formation mechanism and kinetics. Notably, in the mechanochemical chlorination reaction of hydantoin, normalizing kinetic profiles to the volume of the milling ball unequivocally demonstrates the conservation of milling reaction kinetics. In this ball-milling transformation, physical kinetics outweigh chemical factors in determining reaction rates. Attempting to interpret such kinetics solely through chemical terms poses a risk of misinterpretation. Our results highlight that time-resolved in situ investigations of milling reactions provide a novel avenue for fine-tuning and optimizing mechanochemical processes.
Investigating the mechanism and kinetics of the mechanochemical synthesis of multi-component systems
(2023)
Mechanochemistry is a promising and environmentally friendly approach for synthesizing (novel) multicomponent crystal systems. Various milling parameters, such as milling frequency, milling time, and ball diameter have been shown to influence the mechanisms and rates of product formation. Despite increasing interest in mechanochemistry, there is still limited understanding of the underlying reactivity and selectivity mechanisms.
Various analytical techniques have been developed to gain insight into the mechanochemical transformations, including powder X-ray diffraction, X-ray adsorption spectroscopy, NMR, Raman spectroscopy and thermography. Using these techniques, we have studied the formation of (polymorphic) cocrystals, organometallic compounds and salts, and elucidated the influence of milling parameters and reaction sequences on the formation mechanism and kinetics.
For example, our study of the mechanochemical chlorination reaction of hydantoin revealed that normalisation of the kinetic profiles to the volume of the grinding ball clearly showed that physical kinetics dominate the reaction rates in a ball-milling transformation. Attempts to interpret such kinetics in purely chemical terms risk misinterpretation of the results.
Our results suggest that time-resolved in situ investigation of milling reactions is a promising way to fine-tune and optimise mechanochemical processes.
Mechanochemistry emerges as a potent, environmentally friendly, and straightforward approach for crafting novel multicomponent crystal systems. Various milling parameters, including milling frequency, time, filling degree of the milling jar, ball diameter, vessel size, degree of milling ball filling, and material of jars, are recognized influencers on the mechanisms and rates of product formation. Despite the growing interest in mechanochemistry, there exists a gap in understanding the mechanistic aspects of mechanochemical reactivity and selectivity. To address this, diverse analytical methods and their combinations, such as powder X-ray diffraction, X-ray absorption spectroscopy, NMR, Raman spectroscopy, and thermography, have been developed for real-time, in situ monitoring of mechanochemical transformations.
This discussion centers on our recent findings, specifically investigating the formation of (polymorphic) cocrystals and metal-organic frameworks. Through these studies, we aim to unravel the impact of milling parameters and reaction sequences on the formation mechanism and kinetics. Notably, in the mechanochemical chlorination reaction of hydantoin, normalizing kinetic profiles to the volume of the milling ball unequivocally demonstrates the conservation of milling reaction kinetics. In this ball-milling transformation, physical kinetics outweigh chemical factors in determining reaction rates. Attempting to interpret such kinetics solely through chemical terms poses a risk of misinterpretation. Our results highlight that time-resolved in situ investigations of milling reactions provide a novel avenue for fine-tuning and optimizing mechanochemical processes.
Bonds and local atomic environments are crucial descriptors of material properties. They have been used to create design rules and heuristics for materials. More and more frequently, they are used as features in machine learning. Implementations and algorithms (e.g., ChemEnv and LobsterEnv) for identifying these local atomic environments based on geometrical characteristics and quantum-chemical bonding analysis are nowadays available. Fully automatic workflows and analysis tools have been developed to use quantum-chemical bonding analysis on a large scale and for machine-learning approaches. The latter relates to a general trend toward automation in density functional-based materials science. The lecture will demonstrate how our tools, that assess local atomic environments, helped to test and develop heuristics and design rules and an intuitive understanding of materials.
Mechanochemistry is an effective, environmentally benign, and facile method for the synthesis of new multicomponent crystal systems. Different milling parameters are known to affect the mechanisms and rates of product formation: milling frequency, milling time, filling degree of the milling jar, ball diameter and vessel size, degree of milling ball filling, and material of jars. The increasing interest in mechanochemistry is contrasted by a limited mechanistic understanding of the mechanochemical reactivity and selectivity.
Different analytical methods and their combinations have been developed for the time resolved in situ monitoring of mechanochemical transformations, including powder X-ray diffraction, X-ray adsorption spectroscopy, NMR, Raman spectroscopy, and thermography.
Here we will discuss our recent results investigating the formation of (poly-morphic) cocrystals[1,3], metal-organic compounds, and salts, thereby elucidating the influence of milling parameters and reaction sequences on the formation mechanism and kinetics. For the mechanochemical chlorination reaction of hydantoin normalizing the kinetic profiles to the volume of the milling ball showed clearly that milling reaction kinetics are conserved. Here physical kinetics dominate reaction rates in a ball-milling transformation. Attempting to interpret such kinetics in purely chemical terms risk misinterpreting the results.
Our results indicate that time-resolved in situ investigation of milling reactions offer a new approach to tune and optimize mechanochemical processes.
A cobalt(III) complex, [Co(L)3](DMF) (1) of Schiff base ligand HL, 2-((E)-(benzylimino)methyl)-4-bromophenol is prepared and single crystal X-ray structural analysis have also been performed. The structures of complex 1 showed hexa-coordinated mononuclear systems that adopt octahedral geometry. The complex has also exhibited the supramolecular networks through non-covalent interactions like H-bonding, C–Hπ stacking. Moreover, the complex 1 is very effective in the catalytic fixation of carbon dioxide in propergyl alcohols to produce α-alkylidene cyclic carbonates. The catalytic production of α-alkylidene cyclic carbonates have been carried out through carboxylative cyclization of propargyl alcohols using CO2 balloon of 1 atm pressure at 80 ◦C. Solvent free condition (green synthesis) made this catalytic protocol eco-friendly towards the environment. Utilizing various substrates of propargyl alcohols moderate to high percentage yield (62–95%) of respective α-alkylidene cyclic carbonates product have been isolated over this catalytic reaction. Besides, the theoretical calculations (DFT) was performed for the prediction of probable mechanism of the catalytic reaction