TY - CONF A1 - Bustamante, Joana A1 - Naik, Aakash Ashok A1 - Ertural, Christina A1 - Ueltzen, Katharina 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, 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 T2 - 16th International conference on materials chemistry (MC16) CY - Dublin, Ireland DA - 03.07.2023 KW - Thermoelectric materials KW - DFT KW - QHA KW - Grüneisen parameter PY - 2023 AN - OPUS4-57887 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF 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 N2 - 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. T2 - 16th International conference on materials chemistry (MC16) CY - Dublin, Ireland DA - 03.07.2023 KW - Bonding analysis KW - Automation KW - Materials Informatics KW - Computational Chemistry KW - Database PY - 2023 AN - OPUS4-57889 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Radnik, Jörg A1 - Ciornii, Dmitri A1 - Knigge, Xenia A1 - Hodoroaba, Vasile-Dan A1 - Bennet, F. A1 - Meirer, F. A1 - Duijndam, A. A1 - Thünemann, Andreas A1 - Drexel, R. A1 - Fadda, M. A1 - Sacco, A. A1 - Giovannozzi, A.M. A1 - Donskyi, I. A1 - Schusterbauer, R. A1 - Nickl, P. A1 - Reichardt, P. A1 - Altmann, Korinna T1 - Revealing surface functionalities of micro- and nanoplastic particles’ surface by means of XPS N2 - Over the last 20 years, many researchers, politicians, and citizens themselves have become increasingly aware of the growing plastic problem of our time. Inadequate recycling concepts, collection points, and careless dumping of plastic products in the environment lead to an accumulation of plastic. External weather influences can cause these to degrade and fractionate, so that today microplastics (1-1000 µm, ISO/TR 21960:2020) [1] of different polymer materials can be detected in all parts of the world. The precautionary principle applies to microplastics. The particles can break down further to form nanoplastics (<1 µm, ISO/TR 21960:2020) [1]. Whether microplastics or nanoplastics pose a toxicological hazard is being investigated in a variety of ways. Valid results are still pending, however, investigations into the frequency, transport, possible sinks and entry paths must be taken into account. This is why monitoring of microplastics is already required in the revision of the Drinking Water Framework Directive [2]. The same is still pending in the final version of the revision of the Waste Water Framework Directive this year, but is expected. Nanoplastics are particularly under discussion for having a toxic effect on humans and animals, as these particles are small enough to be absorbed by cells. For targeted toxicological studies, it is important to have test and reference materials that resemble the particles found in the environment. To mimic environmental samples, these materials should also have an irregular shape and show aging at the surface, which can be detected with XPS or SEM/EDS. BAM in collaboration with the EMPIR project "PlasticTrace" works on a reference material candidate of nano-sized polypropylene (nano-PP) [3]. The nano-PP vials were tested for homogeneity with PTA and further characterized with bulk and surface-sensitive techniques. An SEM image and a corresponding XPS spectrum are presented in Figure 1. Raman measurements as well as XPS indicate an aged surface. T2 - ECASIA 2024 CY - Gothenburg, Sweden DA - 09.06.2024 KW - Surface chemistry KW - Micro- and nanoplastics KW - X-ray Photoelectron Spectroscopy KW - MNP production technique PY - 2024 AN - OPUS4-60535 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Amariamir, Sasan A1 - Benner, Philipp A1 - George, Janine T1 - Prediction of materials synthesizability N2 - In the pursuit of discovering materials with desirable properties, extending the available material libraries is crucial. High-throughput simulations have become an integral part in designing new materials in the past decades. However, there is no straightforward way of distinguishing synthesizable materials from all the proposed candidates. This project focuses on employing AI-driven methods to estimate synthesizability of materials. Up to now, material scientists and engineers have relied on domain knowledge as well as empirical heuristics to guess the stability and synthesizability of molecules and crystals. The famous Pauling rules of crystal stability are an example of such heuristics. However, after the accelerating material discovery in all the years since Pauling, these rules now fail to account for the stability of most known crystals. A new predictive set of heuristics for crystal stability/synthesizability is unlikely to be uncovered by human perception, given the magnitude and dimensionality of crystallographic data. Hence, a data-driven approach should be proposed to find a predictive model or set of heuristics which differentiate synthesizable crystal structures from the rest. The main challenge of this research problem is the lack of a negative set for classification. Here, there are two classes of data: the positive class which contains synthesizable materials and the negative class which contains materials which are not synthesizable. While the data from the positive class is simply the data of crystals which have been experimentally synthesized, we do not have access to data points which are certainly unsynthesizable. Strictly speaking, if an attempt of synthesizing a crystal fails, it does not necessarily follow that the crystal is not synthesizable. Also, there is no database available which contains the intended crystal structures of unsuccessful synthesis attempts. This project proposes a semi-supervised learning scheme to predict crystal synthesizability. The ML model is trained on experimental and theoretical crystal data. The initial featurization focuses on local environments which is inspired by the Pauling Rules. The experimental data points are downloaded through the Pymatgen API from the Materials Project database which contains relaxed structures recorded in Inorganic Crystal Structure Database – ICSD. The theoretical data is queried from select databases accessible through the Optimade project’s API. T2 - MSE Congress 2022 CY - Darmstadt, Germany DA - 27.09.2022 KW - Synthesizability KW - PU Learning KW - Cheminformatics PY - 2022 AN - OPUS4-56731 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Wurzler, Nina A1 - de Oliveira Guilherme Buzanich, Ana A1 - Radtke, Martin A1 - Reinholz, Uwe A1 - Emmerling, Franziska A1 - Kunte, Hans-Jörg A1 - Özcan Sandikcioglu, Özlem T1 - Chemical interaction mechanisms of metal reducing bacteria on steel surfaces N2 - Metal reducing bacteria (MRB) are able to utilize various materials such as iron, uranium and manganese as well as many organic compounds as electron acceptors. This process leads to the conversion of Fe(III) containing passive film species to soluble Fe(II) oxides and hydroxides. The reduction process triggers the acceleration of general and local corrosion processes. Electron transfer mechanisms are not yet fully understood. In literature it has been controversially discussed to which extend secreted electron shuttles contribute to the extracellular electron transfer (EET). To understand the chemical and electrochemical interaction mechanisms of MRB with steel surfaces this project combines a variety of in-situ techniques. The changes in oxide chemistry on Fe/steel surfaces in the presence of biomolecules and MRB are under investigation using a newly designed electrochemical cell for in situ XANES (x-ray absorption near edge structure) spectroscopy. Electrochemical quartz crystal microbalance (eQCM) studies support the spectroscopic investigations to gain information about the kinetics of attachment processes and changes in biofilm viscosity. The biofilm structure and composition as well as cell viability are investigated by complementary ex situ spectroscopic and microscopic analysis. Combining spectroscopic techniques and eQCM data with electrochemical measurements, biological processes and the resulting degradation of steel surfaces can be observed in a non-destructive manner. Selecting model systems and a defined biological medium allows the determination of individual effects of diverse surface and environmental parameters. The fundamental understanding of bacterial attachment mechanisms and initial steps of biofilm formation will contribute to the development of new antifouling strategies. T2 - 12th International Conference on Biology and Synchrotron Radiation CY - San Francisco, CA, USA DA - 21.08.2016 KW - Microbially influenced corrosion (MIC) KW - Metal reducing bacteria KW - XANES KW - Electrochemistry PY - 2016 AN - OPUS4-37238 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Naik, Aakash Ashok A1 - George, Janine T1 - New descriptors for materials properties based on bonding indicators N2 - Includes a summary of the Ph.D. project that deals with generating a database populated with materials bonding properties and how we intend to gain deeper insights into material properties through this research. T2 - SALSA 2022 CY - Berlin, Germany DA - 15.09.2022 KW - Bonding analysis KW - Automation KW - Materials Informatics KW - Computational Chemistry PY - 2022 AN - OPUS4-56142 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 - TY - CONF A1 - Naik, Aakash Ashok A1 - George, Janine T1 - Building quantum chemical orbital based bonding descriptor database N2 - Motivation, methodology and and results of our quantum chemical bonding descriptors database presented in form of a Poster T2 - RSC Twitter Conference 2023 CY - Online meeting DA - 28.02.2023 KW - Bonding analysis KW - Automation KW - Materials Informatics KW - Computational Chemistry KW - Database PY - 2023 UR - https://twitter.com/NaikAak/status/1630400167080869893 UR - https://twitter.com/NaikAak/status/1630540436434558977 AN - OPUS4-57101 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -