TY - CONF A1 - George, Janine T1 - Accelerated Materials Discovery Based on Data Analysis and Machine Learning N2 - The stability of materials is a very important property for materials discovery. In recent years, we have explored several approaches for advancing the prediction of the stability of crystals (e.g., classical chemical heuristics and machine-learned interatomic potentials). For example, we have assessed a famous chemical heuristic – the Pauling rules – regarding their usefulness for structure prediction. They are only of limited predictive power, i.e. the rules 2 to 5 only work for 13 % of all tested oxides. Furthermore, we have shown that machine-learned interatomic potentials can be used to predict phonon properties of a range of silicon allotropes accurately and, therefore, they can also be used to assess the dynamic stability of materials. To do so, we have developed new schemes to build reference databases for machine learned interatomic potentials. T2 - PACRIM & GOMD 2021 CY - Online meeting DA - 13.12.2021 KW - Data analysis KW - Machine learning KW - High-throughput computations PY - 2021 AN - OPUS4-54088 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - George, Janine T1 - Mit Hilfe künstlicher Intelligenz neue Materialien finden N2 - Dieser Vortrag wurde im Rahmen einer Outreach-Veranstaltung für Schüler*innen gehalten (https://www.hansenberg.de/lernen/hansenberg-summit/summit-2022.html). Er beschreibt, wie wir an der BAM und an anderen Institutionen nach neuen Materialien suchen. T2 - Hansenberg Summit CY - Geisenheim, Germany DA - 09.07.2022 KW - Materialdesign KW - Materialinformatik PY - 2022 AN - OPUS4-55408 LA - deu 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 - 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 - 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 - 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 - 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 -