TY - JOUR A1 - Naik, Aakash A1 - Ertural, Christina A1 - Dhamrait, Nidal A1 - Benner, Philipp A1 - George, Janine T1 - A Quantum-Chemical Bonding Database for Solid-State Materials N2 - 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. KW - Bonding Analysis KW - DFT KW - High-throughput KW - Database KW - Phonons KW - Machine Learning PY - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:b43-582892 VL - 10 IS - 1 SP - 1 EP - 18 AN - OPUS4-58289 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Katsikini, M. A1 - Filintoglou, K. A1 - Pinakidou, F. A1 - Pilatsikas, N. A1 - Arvanitidis, J. A1 - Christofilos, D. A1 - Paloura, E.C. A1 - Ves, S. A1 - Reinholz, Uwe A1 - Papadomanolaki, E. A1 - Lliopoulos, E. T1 - Scaling of phonon frequencies and electron binding energies with interatomic distances in InxGa1−xN N2 - In-K-edge x-ray absorption fine structure (EXAFS), x-ray photoelectron, and Raman spectroscopy results are combined for a comprehensive study of InxGa1−xN layers with energy gaps extending over nearly the whole visible spectrum. The In–N and In–(In,Ga) distances determind by EXAFS are used for the derivation of the In–N bond ionicity as well as for the phonon frequency dependence of the LO and B21 modes, assessed by Raman, and the N 1s, In 3d5/2, Ga 3s, and Ga 2p1/2 electron binding energies on those distances. Phonon confinement due to perturbation of the periodic potential caused by the alloying is also quantified. KW - Band gap KW - Phonons KW - Epitaxy KW - Electronic structure KW - X-ray photoelectron KW - Nitrides PY - 2021 U6 - https://doi.org/10.1063/5.0069359 VL - 130 IS - 20 SP - 205706 - 1 EP - 205706 - 9 PB - AIP Publishing AN - OPUS4-53956 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - GEN 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 0.1cm−1. KW - DFT KW - Quasi-harmonic approximation KW - Raman KW - Phonons KW - Anharmonicity PY - 2023 UR - https://arxiv.org/abs/2305.18931 SP - 1 EP - 14 AN - OPUS4-58369 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Hildebrandt, R A1 - Seifert, M A1 - George, J 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 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 - Thermoelectrics KW - Thermal transport KW - Phonon interactions KW - Density functional theory KW - Phonons PY - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:b43-593164 SN - 1367-2630 VL - 25 IS - 12 SP - 1 EP - 12 PB - IOP Publishing AN - OPUS4-59316 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - GEN A1 - George, Janine A1 - Naik, Aakash Ashok T1 - DFT raw data for "Investigations on electron beam irradiated rare-earth doped SrF2 for application as low fading dosimeter material: Evidence for and DFT simulation of a radiation-induced phase" N2 - DFT raw data for the publication https://doi.org/10.1039/D2TC01773C It includes all predicted structures, the structure optimizations and the phonon computations in VASP format. Phonon computations were performed with the finite displacement method. KW - DFT KW - Phonons KW - Structure prediction PY - 2022 U6 - https://doi.org/10.5281/zenodo.5942729 PB - Zenodo CY - Geneva AN - OPUS4-55417 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - GEN A1 - George, Janine T1 - DFT-based Phonon-computations for "Considering the Role of Ion Transport in Diffuson-Dominated Thermal Conductivity" N2 - These are the harmonic phonon and Grüneisen parameter computations for the publication "Considering the Role of Ion Transport in Diffuson-Dominated Thermal Conductivity" (https://doi.org/10.1002/aenm.202200717). VASP and Phonopy outputs are included in this data set. KW - Thermal conductivity KW - Phonons PY - 2021 U6 - https://doi.org/10.5281/zenodo.5116360 PB - Zenodo CY - Geneva AN - OPUS4-55168 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - George, Janine T1 - Data-driven chemical understanding N2 - Chemical heuristics are essential to understanding molecules and materials in chemistry. The periodic table, atomic radii, and electronegativities are only a few examples. Initially, they have been developed by a combination of physical insight and a limited amount of data. It is now possible to test these heuristics and generate new ones using automation based on Materials Informatic tools like pymatgen and greater amounts of data from databases such as a Materials Project. In this session, I'll speak about heuristics and design rules based on coordination environments and the concept of chemical bonding. For example, we have tested the Pauling rules which describe the stability of materials based on coordination environments and their connections on 5000 oxides from the Materials Project. In addition, we have created automated processes for analyzing the chemical bonding situation in crystalline materials with Lobster (www.cohp.de) in order to discover new heuristics and design rules. T2 - Materials Project Seminar Series CY - Online meeting DA - 18.05.2022 KW - DFT KW - Chemical heuristics KW - Crystal Orbital Hamilton Populations KW - Machine learning KW - Phonons PY - 2022 UR - https://www.youtube.com/watch?v=e7zYrz6fgog UR - https://next-gen.materialsproject.org/community/seminar AN - OPUS4-55008 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 - Ertural, Christina A1 - George, Janine T1 - Vibe Check via Machine Learning: Testing the Prototype N2 - 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. T2 - #RSCPoster Twitter conference 2023 CY - Online meeting DA - 28.02.2023 KW - Interatomic potentials KW - Machine learning KW - Phonons KW - Workflow PY - 2023 UR - https://twitter.com/cer5814012/status/1630547004462858240 AN - OPUS4-57059 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Ertural, Christina A1 - Deringer, V. L. A1 - George, Janine T1 - Automation of machine learning driven interatomic potential generation for predicting vibrational properties N2 - 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. T2 - 16th International conference on materials chemistry (MC16) CY - Dublin, Ireland DA - 03.07.2023 KW - Interatomic potentials KW - Machine learning KW - Phonons KW - Thermoelectrics PY - 2023 AN - OPUS4-57932 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -