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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.
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.
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.
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.
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
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.
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.
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.
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.
This paper reports on the effect of the solvent viscosity on the formation of gold nanoparticles (Au NPs) during Sputtering onto Liquid (SoL) process. All other parameters related to the plasma and the host liquid are kept constant. SoL is a simple highly reproducible approach for preparation of colloidal dispersions of small naked NPs. The properties of the final product are determined by both the sputtering parameters and the host liquid characteristics. As a model system we chose to sputter a gold target by a direct-current magnetron discharge onto a line of polymerized rapeseed oils having similar surface tension (32.6 ― 33.1 mJ·m-2 at RT). It was found that well dispersed Au NPs grow in the bulk solution of oils with low viscosities (below 630 cP at 25 °C) while gold films form onto the surface of high viscosity liquids (more than 1000 cP at 25 °C). The mean diameter of the individual Au NPs slightly increases with oil viscosity and is in range about 2.1―2.5 nm according to transmission electron microscopy.
The talk gives a short overview and explains basic principles of research ethics for PhD students. We examine the most important principles in the codes of conduct for good scientific practice on EU and national level. Based on these general rules, we give some hands-on practical tips for daily scientific work and discuss in detail one of the fields most susceptible to conflict, the topic of authorship in scientific publications.
To date, there are very few technologies available for the conversion of low-temperature waste heat into electricity. Thermomagnetic generators are one approach proposed more than a century ago. Such devices are based on a cyclic change of magnetization with temperature. This switches a magnetic flux and, according to Faraday’s law, induces a voltage.
Here we give an overview on our research, covering both materials and systems. We demonstrate that guiding the magnetic flux with an appropriate topology of the magnetic circuit improves the performance of thermomagnetic generators by orders of magnitude. Through a combination of experiments and simulations, we show that a pretzel-like topology results in a sign reversal of the magnetic flux. This avoids the drawbacks of previous designs, namely, magnetic stray fields, hysteresis and complex geometries of the thermomagnetic material.
Though magnetocaloric materials had been the first choice also for thermomagnetic generators, they require some different properties, which we illustrate with Ashby plots for materials selection. Experimentally we compare La-Fe-Co-Si and Gd plates in the same thermomagnetic generator. Furthermore, we discuss corrosion and deterioration under cyclic use is a severe problem occurring during operation. To amend this, composite plates using polymer as a matrix have been suggested previously.
Mechanochemistry has proven to be a highly effective method for the synthesis of organic compounds. We studied the kinetics of the catalyst-free Knoevenagel reaction between 4-nitrobenzaldehyde and malononitrile, activated and driven by ball milling. The reaction was investigated in the absence of solvents (neat grinding) and in the presence of solvents with different polarities (liquid-assisted grinding). The reaction was monitored using time-resolved in situ Raman spectroscopy and powder X-ray diffraction (PXRD). Our results indicate a direct relationship between solvent polarity and reaction kinetics, with higher solvent polarity leading to faster product (2-(4-nitrobenzylidone)malononitrile) formation. For the first time, we were able to isolate and determine the structure of an intermediate 2-(hydroxy(4-nitrophenyl)methyl)malononitrile based on PXRD data.
Noble metal-free nanoparticles (NPs) based on multi-principal element alloys (MPEAs) were synthesized using a one-step pulsed laser ablation in liquids (PLALs) method for the electrochemical reduction of CO2. Laser ablation was performed in pure water or poly-(diallyldimethylammonium chloride) (PDADMAC)-containing an aqueous solution of Al8Cr17Co17Cu8Fe17Ni33 MPEA targets. Transmission electron microscopy (TEM) measurements combined with energy dispersive X-ray (EDX) mapping were used to characterize the structure and composition of the laser-generated MPEA nanoparticles (MPEA-NPs). These results confirmed the presence of a characteristic elemental distribution of a core-shell phase structure as the predominant NP species. The electrocatalytic performance of the laser-generated MPEA-NPs was characterized by linear sweep voltammetry (LSV) demonstrating an enhanced electrocatalytic CO2 activity for PDADMAC-stabilized NPs. The findings of these investigations indicate that MPEAs have great potential to replace conventional, expensive noble metal electrocatalysts.
Proton exchange membrane fuel cells (PEMFCs) are one of the most promising alternative green energy technologies that deliver high energy density without CO2 emissions. The proton conductivity of proton exchange membranes (PEM) contributes to the overall efficiency of a PEMFC. Materials being used as PEMs must exhibit high proton conductivity at the working conditions of the targeted PEMFC. To date, Nafion and Nafion-like polymers with acidic functionality are widely used as membrane materials due to their high proton conductivity in the range of 10-1 to 10-2 Scm-1 at higher relative humidity. However, these materials suffer from high costs, hazardous production process, and poor performance at high temperatures, limiting their versatility.
In this context, crystalline porous materials are recognized as promising proton conductors for the proton exchange membrane (PEM) in fuel cell technology, owing to their tunable framework structure. However, it is still challenging bulk synthesis for real-world applications of these materials. Herein, we present mechanochemical gram-scale synthesis of series of mixed ligand metal organic frameworks (MOFs) and metal hydrogen‐bonded organic frameworks (MHOFs) using phenylene diphosphonic acid and 1-hydroxyethylidene-1,1-diphosphonic acid with different bipyridyl type of ligands, respectively. In all cases, the existence of extensive hydrogen bonds with amphiprotic uncoordinated phosphonate hydroxyl and oxygen atoms, the frameworks exhibited high proton conductivity. The study demonstrates the potential of green mechanosynthesis for preparations of framework-based proton conducting materials in bulk scale for green energy generation.
Vibrational properties play a key role in determining the stability and thermal conductivity behaviour of materials. The quasi- harmonic approximation gives insight into the phononic properties of a compound, but in the established way, i.e. density functional theory based methods, it takes many calculation steps and consumes a lot of resources to arrive at the desired results. Machine learning (ML) trained interatomic potentials (e.g. Gaussian approximation potential, GAP) pose an alternative to the traditional computation way of phonons. We develop a Python code based workflow which combines automation tools like atomate2 with ML to ease providing interactomic potentials for (quantum chemical) computations and databases.
Chemical bonding and coordination environments are crucial descriptors of material properties. They have previously been applied to creating chemical design guidelines and chemical heuristics. They are currently being used as features in machine learning more and more frequently. I will discuss implementations and algorithms (ChemEnv and LobsterEnv) for identifying these coordination environments based on geometrical characteristics and chemical bond quantum chemical analysis. I will demonstrate how these techniques helped in testing chemical heuristics like the Pauling rule and thereby improved our understanding of chemistry. I will also show how these tools can be used to create new design guidelines and a new understanding of chemistry. To use quantum-chemical bonding analysis on a large-scale and for machine-learning approaches, fully automatic workflows and analysis tools have been developed. After presenting the capabilities of these tools, I will also point out how these developments relate to the general trend towards automation in the field of density functional based materials science.
Mechanically flexible single crystals of molecular materials offer potential for a multitude of new directions in advanced materials design. Before the full potential of such materials can be exploited, insight into their mechanisms of action must be better understood. Such insight can be only obtained through synergistic use of advanced experimentation and simulation. We herein report the first detailed mechanistic study of elasto-plastic flexibility in a molecular solid. An atomistic origin for this mechanical behaviour is proposed through a combination of atomic force microscopy, μ-focus synchrotron X-ray diffraction, Raman spectroscopy, ab initio simulation, and computed elastic tensors. Our findings suggest that elastic and plastic bending are intimately linked and result from extensions of the same molecular deformations. The proposed mechanism bridges the gap between contested mechanisms, suggesting its applicability as a general mechanism for elastic and plastic bending in organic molecular crystals.
Crystalline porous materials are recognized as promising proton conductors for the proton exchange membrane (PEM) in fuel cell technology owing to their tunable framework structure. However, it is still a challenging bulk synthesis for real-world applications of these materials. Herein, we report the mechanochemical gram-scale synthesis of two isostructural metal hydrogen-bonded organic frameworks (MHOFs) of Co(II) and Ni(II) based on 1-hydroxyethylidenediphosphonic acid (HEDPH4) with 2,2′-bipyridine (2,2′-bipy): Co(HEDPH3)2(2,2′-bipy)·H2O (1) and Ni(HEDPH3)2(2,2′-bipy)·H2O (2). In situ monitoring of the mechanochemical synthesis using different synchrotron-based techniques revealed a one-step mechanism – the starting materials are directly converted to the product. With the existence of extensive hydrogen bonds with amphiprotic uncoordinated phosphonate hydroxyl and oxygen atoms, both frameworks exhibited proton conduction in the range of 10–4 S cm–1 at room temperature under humid conditions. This study demonstrates the potential of green mechanosynthesis for bulk material preparation of framework-based solid-state proton conductors.
Soda lakes of the East African Rift Valley are hyperalkaline, hypersaline lakes extremely enriched in Na+, K+, Cl−, CO32−, HCO3−, and SiO2. In this paper, we investigate the chemical evolution in these lakes and the production of chemical sediments by salt precipitation via evaporation. Water samples from tributary springs and three lakes (Magadi, Nasikie Engida and Natron) have been experimentally studied by in-situ X-ray diffraction during evaporation experiments to characterize the sequence of mineral precipitation. These data are complemented by ex-situ diffraction studies, chemical analyses and thermodynamic hydrochemical calculations producing detailed information on the activity of all solution species and the saturation state of all minerals potentially generated by the given composition. Major minerals precipitating from these samples are sodium carbonates/bicarbonates as well as halite. The CO3/HCO3 ratio, controlled by pH, is the main factor defining the Na‑carbonates precipitation sequence: in lake brines where CO3/HCO3 > 1, trona precipitates first whereas in hot springs, where CO3/HCO3 ≪ 1, nahcolite precipitates instead of trona, which forms later via partial dissolution of nahcolite. Precipitation of nahcolite is possible only at lower pH values (pCO2 higher than −2.7) explaining the distribution of trona and nahcolite in current lakes and the stratigraphic sequences. Later, during evaporation, thermonatrite precipitates, normally at the same time as halite, at a very high pH (>11.2) after significant depletion of HCO3− due to trona precipitation. The precipitation of these soluble minerals increases the pH of the brine and is the main factor contributing to the hyperalkaline and hypersaline character of the lakes. Villiaumite, sylvite, alkaline earth carbonates, fluorapatite and silica are also predicted to precipitate, but most of them have not been observed in evaporation experiments, either because of the small amount of precipitates produced, kinetic effects delaying the nucleation of some phases, or by biologically induced effects in the lake chemistry that are not considered in our calculations. Even in these cases, the chemical composition in the corresponding ions allows for discussion on their accumulation and the eventual precipitation of these phases. The coupling of in-situ and ex-situ experiments and geochemical modelling is key to understanding the hydrogeochemical and hydroclimatic conditions of soda lakes, evaporite settings, and potentially soda oceans of early Earth and other extraterrestrial bodies.
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, other recent workflow contributions to the Materials Project software infrastructure (pymatgen, atomate2) related to phonons and machine-learning potentials will be discussed.
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.
Investigating the phononic properties is beneficial for predicting low thermal conductivity thermoelectric materials.1–3 Employing density functional theory4 takes many calculation steps and consumes a lot of computational resources.5,6 Using machine learning driven interatomic potentials (MLIP, e.g., Gaussian approximation potential,8 GAP) opens up a faster route to phonons7 but in most cases, the potentials are specifically tailored for a certain compound. In this work, we automate the generation of such MLIPs in a Python code-based workflow, based on the automation tools atomate29 and pymatgen10 which combines the automatic DFT computations with the automated fitting of GAPs. Automation enables easier testing, benchmarking and validation.11 We aim to provide the workflow-generated potentials for storage in databases.
Argyrodite-type materials have lately sparked a lot of research interest due to their thermoelectric properties.One promising candidate is canfieldite (Ag8SnS6), which has a Pna21 orthorhombic crystal structure at room temperature (RT). Recently, a new low-temperature (LT) phase transition of canfieldite at 120K has been found. Here, we investigate structural, vibrational and thermodynamic properties of Ag8SnS6 at room- and low-temperature employing density functional theory (DFT) and lattice dynamics computations. Thermal properties calculations were based on the quasi-harmonic approximation (QHA) as implemented in phonopy. We achieve good agreement with experiments. Thermal properties such as the constant-pressure heat capacity (Cp) and thermal conductivity are very close to experimental measurements. Furthermore, we compared RT and LT Ag8SnS6 Grüneisen parameters with argyrodites analogues, Ag8XS6 (X = Sn, Si, Ge), to arrive at an improved
Understanding the chemistry and nature of individual chemical bonds is essential for materials design. Bonding analysis via the LOBSTER software package has provided valuable insights into the properties of materials for thermoelectric and catalysis applications. Thus, the data generated from
bonding analysis becomes an invaluable asset that could be utilized as features in large-scale data analysis and machine learning of material properties. However, no systematic studies exist that conducted high-throughput materials simulations to curate and validate bonding data obtained from LOBSTER. Here we present an approach to constructing such a large database consisting of quantum-chemical bonding information.
In the current study, we investigate an interaction under high-pressure high-temperature of single phase fcc-, hcp- and bcc-structured high-entropy alloys with hydrogen, carbon and nitrogen to obtain high-entropy hydrides, carbides and nitrides. Structural changes in high-entropy alloys upon compression and heating in the presence of these light elements are in the focus of our investigation. An easy route to high-entropy hydrides, carbides and nitrides will open new synthetic horizons in compositionally complex materials. Our study suggests that high-entropy alloys form high- entropy hydrides mainly with a composition close to M:H 1:1 ratio. Hydrides can be obtained under compression with hydrogen as a pressure compression medium or using hydrogen fluid as reactive agent.
New insights into pertinent Fe-complexes for the synthesis of iron via the instant polyol process
(2023)
Chemically synthesized iron is in demand for biomedical applications due to its large saturation magnetization compared to iron oxides. The polyol process, suitable for obtaining Co and Ni particles and their alloys, is laborious in synthesizing Fe. The reaction yields iron oxides, and the reaction pathway remains unexplored. This study shows that a vicinal polyol, such as 1,2-propanediol, is suitable for obtaining Fe rather than 1,3-propanediol owing to the formation of a reducible Fe intermediate complex. X-ray absorption spectroscopy analysis reveals the ferric octahedral geometry and tetrahedral geometry in the ferrous state of the reaction intermediates in 1,2-propanediol and 1,3-propanediol, respectively. The final product obtained using a vicinal polyol is Fe with a γ-Fe2O3 shell, while the terminal polyol is favourable for Fe3O4. The distinct Fe–Fe and Fe–O bond lengths suggest the presence of a carboxylate group and a terminal alkoxide ligand in the intermediate of 1,2-propanediol. A large Fe–Fe bond distance suggests diiron complexes with bidentate carboxylate bridges. Prominent high-spin and low-spin states indicate the possibility of transition, which favors the reduction of iron ions in the reaction using 1,2-propanediol.
An in-depth insight into the chemistry and nature of the individual chemical bonds is essential for understanding materials. Bonding analysis is thus expected to provide important features for large-scale data analysis and machine learning of material properties. Such chemical bonding information can be computed using the LOBSTER software package, which post-processes modern density functional theory data by projecting the plane wave-based wave functions onto an atomic orbital basis. With the help of a fully automatic workflow, the VASP and LOBSTER software packages are used to generate the data. We then perform bonding analyses on 1520 compounds (insulators and semiconductors) and provide the results as a database. The projected densities of states and bonding indicators are benchmarked on standard density-functional theory computations and available heuristics, respectively. Lastly, we illustrate the predictive power of bonding descriptors by constructing a machine learning model for phononic properties, which shows an increase in prediction accuracies by 27% (mean absolute errors) compared to a benchmark model differing only by not relying on any quantum-chemical bonding features.
Python Materials Genomics (pymatgen) is a robust materials analysis code that defines classes for structures and molecules with support for many electronic structure codes. This open-source software package powers the Materials Project.
In this particular contribution, the handling of obital-resolved "ICOHPLIST.lobster" files from Lobster was implemented in the software package (github handle: @JaGeo).
The expansive production of data in materials science, their widespread sharing and repurposing requires educated support and stewardship. In order to ensure that this need helps rather than hinders scientific work, the implementation of the FAIR-data principles (Findable, Accessible, Interoperable, and Reusable) must not be too narrow. Besides, the wider materials-science community ought to agree on the strategies to tackle the challenges that are specific to its data, both from computations and experiments. In this paper, we present the result of the discussions held at the workshop on “Shared Metadata and Data Formats for Big-Data Driven Materials Science”. We start from an operative definition of metadata, and the features that a FAIR-compliant metadata schema should have. We will mainly focus on computational materials-science data and propose a constructive approach for the FAIRification of the (meta)data related to ground-state and excited-states calculations, potential-energy sampling, and generalized workflows. Finally, challenges with the FAIRification of experimental (meta)data and materials-science ontologies are presented together with an outlook of how to meet them.
Argyrodite-type materials have lately sparked a lot of research interest due to their thermoelectric properties. One promising candidate is canfieldite (Ag8SnS6), which has a Pna21 orthorhombic crystal structure at room temperature (RT). Recently, Slade group found a new low-temperature (LT) phase transition of canfieldite at 120K. Therefore, we investigate structural, vibrational and thermodynamic properties of Ag8SnS6 at room- and low-temperature employing density-functional theory (DFT) and lattice dynamics computations. Thermal properties calculations were based on the quasi-harmonic approximation (QHA) as implemented in phonopy. We achieve good agreement with experiments. Lattice parameters were overestimated by 2%, and thermal properties such as the constant-pressure heat capacity Cp are very close to experimental measurements. Our simulations also reveal a possible new phase transition at around 312 K. Furthermore, we compared RT and LT Ag8SnS6 Grüneisen parameters with some argyrodites analogues, Ag8TS6 (T = Si, Ge, Ti and Sn), finding a relationship between the anharmonicity and low thermal conductivity.
Zirconium vanadate (ZrV2O7) is a well-known negative thermal expansion (NTE) material that exhibits significant isotropic contraction over a broad temperature range (~150°C < T < 800°C). Therefore, it can be used to create composites with controllable expansion coefficients and prevent thermal stress, fatigue, cracking, and deformation at interfaces. We implement interdisciplinary research to analyze such material. We study the influence of the synthesis methods and their parameters on the sample's purity, crystallinity, and homogeneity. Moreover, we implement ab initio-based vibrational computations with partially treated anharmonicity in combination with experimental methods to follow temperature-induced structural changes and rationalize the negative thermal expansion in this material, including the influence of the local structure disorder.
Argyrodite-type materials have lately sparked a lot of research interest due to their thermoelectric properties. One promising candidate is canfieldite (Ag8SnS6), which has a Pna21 orthorhombic crystal structure at room temperature (RT). Recently, experimentalists have found a new low-temperature (LT) phase transition of canfieldite at 120K. Therefore, we investigate structural, vibrational and thermodynamic properties of Ag8SnS6 at room- and low-temperature employing density-functional theory (DFT) and lattice dynamics computations. Thermal properties calculations were based on the quasi-harmonic approximation (QHA) as implemented in phonopy. We achieve good agreement with experiments. Lattice parameters were overestimated by 2%, and thermal properties such as the constant-pressure heat capacity Cp are very close to experimental measurements. Our simulations also reveal a possible new phase transition at around 312 K. Furthermore, we compared RT and LT Ag8SnS6 Grüneisen parameters with some argyrodites analogues, Ag8TQ6 (T = Si, Ge, Ti and Sn; Q = S, Se), finding a relationship between the anharmonicity and low thermal conductivity.
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 (DFT) 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 0.1cm−1.
Knowing phonon properties is beneficial for predicting low thermal conductivity thermoelectric materials. Employing DFT consumes lots of computational resources. Using ML-driven interatomic potentials (MLIP, e.g., GAP) opens up a faster route, but most potentials are specifically tailored to a certain compound. We aim to generalize the MLIP generation in a Python code-based workflow, combining automatic DFT runs with automated GAP fits. Automation enables easier tests, benchmarks, and validation.
The lattice enthalpies and monotropic relationship of two dehydroepiandrosterone (DEHA) polymorphs (forms I and II) were evaluated through a combination of differential scanning calorimetry (DSC), isothermal solution microcalorimetry, and drop-sublimation Calvet microcalorimetry experiments. The standard molar enthalpy of transition between both forms was determined as ΔtrsHom (II→I, 298.15 K) = - 0.90 ± 0.07 kJ mol-1 and ΔtrsHom (II→I, 417.8 K) = - 1.7 ± 1.0 kJ mol- 1, from measurements of standard molar enthalpies of solution in dimethyl sulfoxide and enthalpies of fusion, respectively. Drop-sublimation Calvet microcalorimetry experiments on form I led to ΔsubHom (cr I, 298.15 K) = 132.0±3.3 kJ mol - 1. This result, when combined with the more precise ΔtrsHom (II→I) value obtained by solution calorimetry, afforded ΔsubHom (cr II, 298.15 K) = 131.1±3.3 kJ mol - 1. The overall data indicate that on enthalpic grounds form I is more stable than form II from 298.15 K up to fusion. This conclusion, and the fact that DSC experiments indicated that form I has also a considerably higher temperature fusion, namely, Tfus(cr I)= 422.5±0.2 K and Tfus(cr II) = 413.1±0.2 K, suggest that the two polymorphs are monotropically related.
A deep insight into the chemistry and nature of individual chemical bonds is essential for understanding materials. Bonding analysis is expected to provide important features for large-scale data analysis and machine learning of material properties. Such information on chemical bonds can be calculated using the LOBSTER (www.cohp.de) software package, which post-processes data from modern density functional theory computations by projecting plane wave-based wave functions onto a local atomic orbital basis. We have performed bonding analysis on 1520 compounds (insulators and semiconductors) using a fully automated workflow combining the VASP and LOBSTER software packages. We then automatically evaluated the data with LobsterPy (https://github.com/jageo/lobsterpy) and provide results as a database. The projected densities of states and bonding indicators are benchmarked on VASP projections and available heuristics, respectively. Lastly, we illustrate the predictive power of bonding descriptors by constructing a machine-learning model for phononic properties, which shows an increase in prediction accuracies by 27 % (mean absolute errors) compared to a benchmark model differing only by not relying on any quantum-chemical bonding features.
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This database consists of bonding data computed using Lobster for 1520 solid-state compounds consisting of insulators and semiconductors. It consists of two kinds of json files. Smaller lightweight JSONS consists of summarized bonding information for each of the compounds. The files are named as per ID numbers in the materials project database.
Here we provide also the larger computational data json files for 700 compounds. This files consists of all important LOBSTER computation output files data stored as dictionary.
This database consists of bonding data computed using Lobster for 1520 solid-state compounds consisting of insulators and semiconductors. The files are named as per ID numbers in the materials project database.
Here we provide the larger computational data JSON files for the rest of the 820 compounds. This file consists of all important LOBSTER computation output files data stored as a dictionary.