TY - GEN A1 - Ghosh, Sheuly A1 - Ueltzen, Katharina A1 - George, Janine A1 - Neugebauer, Jörg A1 - Körmann, Fritz T1 - Chemical ordering and magnetism in face-centered cubic CrCoNi alloy N2 - The impact of magnetism on chemical ordering in face-centered cubic CrCoNi medium entropy alloy is studied by a combination of ab initio simulations, machine learning potentials, and Monte Carlo simulations. Large magnetic energies are revealed for some mixed L12 /L10 type ordered configurations, which are rooted in strong nearest-neighbor magnetic exchange interactions and chemical bonding among the constituent elements. There is a delicate interplay between magnetism and stability of MoPt2 and L12 /L10 type of ordering which may explain opposing experimental and theoretical findings. KW - Bonding analysis KW - Alloys KW - Magnetism KW - Material design PY - 2024 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-600040 DO - https://doi.org/10.21203/rs.3.rs-3978660/v1 SN - 2693-5015 SP - 1 EP - 13 PB - Research Square Platform LLC CY - Durham, NC AN - OPUS4-60004 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Naik, Aakash Ashok A1 - Ueltzen, Katharina A1 - Ertural, Christina A1 - Jackson, Adam J. A1 - George, Janine T1 - LobsterPy: A package to automatically analyze LOBSTERruns N2 - 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. KW - Materials Science KW - Automation KW - Bonding Analysis KW - Materials Properties PY - 2024 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-595809 DO - https://doi.org/10.21105/joss.06286 VL - 9 IS - 94 SP - 1 EP - 4 PB - The Open Journal AN - OPUS4-59580 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Rosen, Andrew S. A1 - Gallant, Max A1 - George, Janine A1 - Riebesell, Janosh A1 - Sahasrabuddhe, Hrushikesh A1 - Shen, Jimmy-Xuan A1 - Wen, Mingjian A1 - Evans, Matthew L. A1 - Petretto, Guido A1 - Waroquiers, David A1 - Rignanese, Gian-Marco A1 - Persson, Kristin A. A1 - Jain, Anubhav A1 - Ganose, Alex M. T1 - Jobflow: Computational Workflows Made Simple N2 - 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. KW - Automation KW - Workflow KW - Computational Materials Science KW - Computations KW - Software PY - 2024 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-593104 DO - https://doi.org/10.21105/joss.05995 VL - 9 IS - 93 SP - 1 EP - 7 PB - The Open Journal AN - OPUS4-59310 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - George, Janine T1 - Data-Driven Chemical Understanding with Bonding Analysis N2 - 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. T2 - Otaniemi Center for Atomic-scale Materials Modeling Seminar CY - Aalto, Finland DA - 11.03.2024 KW - Automation KW - Materials Design KW - Bonding Analysis KW - Machine Learning PY - 2024 AN - OPUS4-59671 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - George, Janine T1 - New Opportunities for Data-Driven Chemistry and Materials Science Through Automation N2 - In recent years, many protocols in computational materials science have been automated and made available within software packages (primarily Python-based). This ranges from the automation of simple heuristics (oxidation states, coordination environments) to the automation of protocols, including multiple DFT and post-processing tools such as (an)harmonic phonon computations or bonding analysis. Such developments also shorten the time frames of projects after such developments have been made available and open new possibilities. For example, we can now easily make data-driven tests of well-known rules and heuristics or develop quantum chemistry-based materials descriptors for machine learning approaches. These tests and descriptors can have applications related to magnetic ground state predictions of materials relevant for spintronic applications or for predicting thermal properties relevant for thermal management in electronics. Combining high-throughput ab initio computations with fitting, fine-tuning machine learning models and predictions of such models within complex workflows is also possible and promises further acceleration in the field. In this talk, I will show our latest efforts to link automation with data-driven chemistry and materials science. T2 - MRS Spring CY - Seattle, Washington, USA DA - 21.04.2024 KW - Automation KW - Workflows KW - Chemical Bonding KW - Thermal Properties KW - Materials Design PY - 2024 AN - OPUS4-59982 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - GEN A1 - Ueltzen, Katharina A1 - George, Janine T1 - Bonding analysis results for "Chemical ordering and magnetism in face-centered cubic CrCoNi alloy" N2 - This repository contains the code and data to produce the results of chapter IIIC. Covalent bonding analysis for L12/L10 type configurations of the publication Chemical ordering and magnetism in face-centered cubic CrCoNi alloy by Sheuly Ghosh et al. KW - Magnetism KW - Bonding Analysis KW - Medium Entropy Alloys PY - 2024 DO - https://doi.org/10.5281/zenodo.11104874 PB - Zenodo CY - Geneva AN - OPUS4-59987 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - GEN A1 - Rosen, Andrew S. A1 - Gallant, Max A1 - George, Janine A1 - Riebesell, Janosh A1 - Sahasrabuddhe, Hrushikesh A1 - Shen, Jimmy-Xuan A1 - Wen, Mingjian A1 - Evans, Matthew L. A1 - Petretto, Guido A1 - Waroquiers, David A1 - Rignanese, Gian-Marco A1 - Persson, Kristin A. A1 - Jain, Anubhav A1 - Ganose, Alex M. T1 - Jobflow: Computational Workflows Made Simple N2 - 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. KW - Automation KW - Workflows KW - Computational Materials Science PY - 2024 DO - https://doi.org/10.5281/zenodo.10466868 PB - Zenodo CY - Geneva AN - OPUS4-59313 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - George, Janine T1 - High-throughput and automated bonding Analysis N2 - Talk as a part of the LOBSTER CECAM SCHOOL. This talk introduced all participants to automation tools around the software LOBSTER. T2 - CECAM LOBSTER School CY - Aalto, Finland DA - 12.03.2024 KW - Automation KW - Workflows KW - Bonding Analysis KW - Materials Design KW - Chemically Complex Materials PY - 2024 AN - OPUS4-59672 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - George, Janine T1 - Data-driven approaches for materials understanding and design N2 - 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 computational materials science and some of our recent contributions will be discussed. T2 - Machine Learning of First Principles Observables CY - Berlin, Germany DA - 08.07.2024 KW - Automation KW - Bonding Analysis KW - Machine Learned Potentials PY - 2024 AN - OPUS4-60660 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - George, Janine T1 - Data-driven chemical understanding with bonding analysis N2 - 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. T2 - Seminar of the Department of Chemistry at Imperial College London CY - Online meeting DA - 20.02.2024 KW - Automation KW - Magnetism KW - Bonding Analysis KW - Machine Learning PY - 2024 AN - OPUS4-59546 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - George, Janine T1 - Data-driven chemical understanding with geometrical and quantum-chemical bonding analysis N2 - Talk about my recent research on data-driven chemical understanding with geometrical and quantum-chemical bonding analysis. T2 - Donnerstagskolloquium IPC/IAAC CY - Münster, Germany DA - 04.05.2023 KW - Automation KW - Machine learning KW - Bonding Analysis PY - 2023 AN - OPUS4-57431 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Miliūtė, Aistė A1 - Bustamante, Joana A1 - Mieller, Björn A1 - Stawski, Tomasz A1 - George, Janine A1 - Knoop, F. T1 - High-quality zirconium vanadate samples for negative thermal expansion (NTE) analysis N2 - Zirconium vanadate (ZrV2O7) is a well-known negative thermal expansion (NTE) material which exhibits significant isotropic contraction over a broad temperature range (~150°C < T < 800°C). The linear thermal expansion coefficient of ZrV2O7 is −7.1×10-6 K-. Therefore, it can be used to create composites with controllable expansion coefficients and prevent destruction by thermal shock. Material characterization, leading to application, requires pure, homogenous samples of high crystallinity via a reliable synthesis route. While there is a selection of described syntheses in the literature, it still needs to be addressed which synthesis route leads to truly pure and homogenous samples. Here, we study the influence of the synthesis methods (solid-state, sol-gel, solvothermal) and their parameters on the sample's purity, crystallinity, and homogeneity. The reproducibility of results and data obtained with scanning electron microscopy (SEM), X-ray diffraction (XRD), differential scanning calorimetry, and thermogravimetric analysis (DSC/TGA) were analyzed extensively. The sol-gel method proves superior to the solid-state method and produces higher-quality samples over varying parameters. Sample purity also plays an important role in NTE micro and macro-scale characterizations that explain the impact of porosity versus structural changes. Moreover, we implement ab-initio-based vibrational computations with partially treated anharmonicity (quasi-harmonic approximation, temperature-dependent effective harmonic potentials) in combination with experimental methods to follow and rationalize the negative thermal expansion in this material, including the influence of the local structure disorder, microstructure, and defects. Khosrovani et al. and Korthuis et al., in a series of diffraction experiments, attributed the thermal contraction of ZrV2O7 to the transverse thermal motion of oxygen atoms in V-O-V linkages. In addition to previous explanations, we hypothesize that local disorder develops in ZrV2O7 crystals during heating. We are working on the experimental ZrV2O7 development and discuss difficulties one might face in the process as well as high-quality sample significance in further investigation. The obtained samples are currently used in the ongoing research of structure analysis and the negative thermal expansion mechanism. T2 - 4th International Symposium on Negative Thermal Expansion and Related Materials (ISNTE-4) CY - Padua, Italy DA - 04.07.2023 KW - NTE KW - Sol-gel KW - Solid-state KW - Ab-initio KW - TDEP PY - 2023 AN - OPUS4-58132 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - GEN A1 - Naik, Aakash Ashok A1 - Ertural, Christina A1 - Dhamrait, Nidal A1 - Benner, Phillip A1 - George, Janine T1 - A Quantum-Chemical Bonding Database for Solid-State Materials (JSONS: Part 1) N2 - 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. KW - Bonding analysis KW - Automation KW - Materials Informatics KW - Computational Chemistry KW - Database PY - 2023 DO - https://doi.org/10.5281/zenodo.7794811 PB - Zenodo CY - Geneva AN - OPUS4-57439 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - GEN A1 - Naik, Aakash Ashok A1 - Ertural, Christina A1 - Dhamrait, Nidal A1 - Benner, Phillip A1 - George, Janine T1 - Quantum-Chemical Bonding Database (Unprocessed data : Part 8) N2 - This data is associated with the manuscript "A Quantum-Chemical Bonding Database for Solid-State Materials." Refer to mpids.txt to see data related to which compounds are available in the tar file. (mp-xxx refer to Materials Project ID) KW - Bonding analysis KW - Automation KW - Materials Informatics KW - Computational Chemistry KW - Database PY - 2023 DO - https://doi.org/10.5281/zenodo.7852823 PB - Zenodo CY - Geneva AN - OPUS4-57448 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - INPR A1 - Hildebrandt, R. A1 - Seifert, M. A1 - George, Janine A1 - Blaurock, S. A1 - Botti, S. A1 - Krautscheid, H. A1 - Grundmann, M. A1 - Sturm, C. T1 - Determination of acoustic phonon anharmonicities via second-order Raman scattering in CuI N2 - 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 0.1 cm⁻¹. KW - DFT KW - Quasi-harmonic approximation KW - Raman KW - Phonons KW - Anharmonicity PY - 2023 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-583699 DO - https://doi.org/10.48550/arXiv.2305.18931 SN - 2331-8422 SP - 1 EP - 14 PB - Cornell University CY - Ithaca, NY AN - OPUS4-58369 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Miliūtė, Aistė A1 - Bustamante, Joana A1 - Mieller, Björn A1 - Stawski, Tomasz A1 - George, Janine A1 - Knoop, F. T1 - High-quality zirconium vanadate samples for negative thermal expansion (NTE) analysis N2 - Zirconium vanadate (ZrV2O7) is a well-known negative thermal expansion (NTE) material which exhibits significant isotropic contraction over a broad temperature range (~150°C < T < 800°C). The linear thermal expansion coefficient of ZrV2O7 is −7.1×10-6 K-1. Therefore, it can be used to create composites with controllable expansion coefficients and prevent destruction by thermal shock. Material characterization, leading to application, requires pure, homogenous samples of high crystallinity via a reliable synthesis route. While there is a selection of described syntheses in the literature, it still needs to be addressed which synthesis route leads to truly pure and homogenous samples. Here, we study the influence of the synthesis methods (solid-state, sol-gel, solvothermal) and their parameters on the sample's purity, crystallinity, and homogeneity. The reproducibility of results and data obtained with scanning electron microscopy (SEM), X-ray diffraction (XRD), differential scanning calorimetry, and thermogravimetric analysis (DSC/TGA) were analyzed extensively. The sol-gel method proves superior to the solid-state method and produces higher-quality samples over varying parameters. Sample purity also plays an important role in NTE micro and macro-scale characterizations that explain the impact of porosity versus structural changes. Moreover, we implement ab-initio-based vibrational computations with partially treated anharmonicity (quasi-harmonic approximation, temperature-dependent effective harmonic potentials) in combination with experimental methods to follow and rationalize the negative thermal expansion in this material, including the influence of the local structure disorder, microstructure, and defects. Khosrovani et al. and Korthuis et al., in a series of diffraction experiments, attributed the thermal contraction of ZrV2O7 to the transverse thermal motion of oxygen atoms in V-O-V linkages. In addition to previous explanations, we hypothesize that local disorder develops in ZrV2O7 crystals during heating. We are working on the experimental ZrV2O7 development and discuss difficulties one might face in the process as well as high-quality sample significance in further investigation. The obtained samples are currently used in the ongoing research of structure analysis and the negative thermal expansion mechanism. T2 - TDEP2023: Finite-temperature and anharmonic response properties of solids in theory and practice CY - Linköping, Sweden DA - 21.08.2023 KW - NTE KW - Sol-gel KW - Solid-state KW - Ab-initio KW - TDEP PY - 2023 AN - OPUS4-58135 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - George, Janine T1 - Data-driven chemical understanding with bonding analysis N2 - 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. T2 - 2023 MRS Fall Meeting & Exhibit CY - Boston, Massachusetts, USA DA - 26.11.2023 KW - Automation KW - Machine learning KW - Materials Understanding KW - Magnetism KW - Phonons PY - 2023 AN - OPUS4-59002 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Bustamante, Joana A1 - Naik, Aakash Ashok A1 - Ueltzen, Katharina A1 - Ertural, Christina A1 - George, Janine T1 - Thermodynamic and Thermoelectric Properties of the Canfieldite, (Ag8SnS6 ), in the Quasi-Harmonic Approximation N2 - 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. T2 - TDEP Summer School 2023 (TDEP2023: Finite-temperature and anharmonic response properties of solids in theory and practice) CY - Linköping, Sweden DA - 20.08.2023 KW - Thermoelectric materials KW - DFT KW - QHA KW - Grüneisen parameter PY - 2023 AN - OPUS4-58147 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Miliūtė, Aistė A1 - George, Janine A1 - Mieller, Björn A1 - Stawski, Tomasz T1 - ZrV2O7 negative thermal expansion (NTE) material N2 - 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. T2 - SALSA Make and Measure Conference CY - Berlin, Germany DA - 13.09.2023 KW - NTE KW - Composites KW - TEM PY - 2023 AN - OPUS4-58367 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Miliūtė, Aistė A1 - Bustamante, Joana A1 - Mieller, Björn A1 - Stawski, Tomasz A1 - George, Janine A1 - Knoop, F. T1 - High-quality zirconium vanadate samples for negative thermal expansion (NTE) analysis N2 - Zirconium vanadate (ZrV2O7) is a well-known negative thermal expansion (NTE) material which exhibits significant isotropic contraction over a broad temperature range (~150°C < T < 800°C). The linear thermal expansion coefficient of ZrV2O7 is −7.1×10-6 K-. Therefore, it can be used to create composites with controllable expansion coefficients and prevent destruction by thermal shock. Material characterization, leading to application, requires pure, homogenous samples of high crystallinity via a reliable synthesis route. While there is a selection of described syntheses in the literature, it still needs to be addressed which synthesis route leads to truly pure and homogenous samples. Here, we study the influence of the synthesis methods (solid-state, sol-gel, solvothermal) and their parameters on the sample's purity, crystallinity, and homogeneity. The reproducibility of results and data obtained with scanning electron microscopy (SEM), X-ray diffraction (XRD), differential scanning calorimetry, and thermogravimetric analysis (DSC/TGA) were analyzed extensively. The sol-gel method proves superior to the solid-state method and produces higher-quality samples over varying parameters. Sample purity also plays an important role in NTE micro and macro-scale characterizations that explain the impact of porosity versus structural changes. Moreover, we implement ab-initio-based vibrational computations with partially treated anharmonicity (quasi-harmonic approximation, temperature-dependent effective harmonic potentials) in combination with experimental methods to follow and rationalize the negative thermal expansion in this material, including the influence of the local structure disorder, microstructure, and defects. Khosrovani et al. and Korthuis et al., in a series of diffraction experiments, attributed the thermal contraction of ZrV2O7 to the transverse thermal motion of oxygen atoms in V-O-V linkages. In addition to previous explanations, we hypothesize that local disorder develops in ZrV2O7 crystals during heating. We are working on the experimental ZrV2O7 development and discuss difficulties one might face in the process as well as high-quality sample significance in further investigation. The obtained samples are currently used in the ongoing research of structure analysis and the negative thermal expansion mechanism. T2 - 16th International conference on materials chemistry (MC16) CY - Dublin, Ireland DA - 03.07.2023 KW - NTE KW - Sol-gel KW - Solid-state KW - Ab-initio KW - TDEP PY - 2023 AN - OPUS4-58134 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -