TY - CONF A1 - Bustamante, Joana A1 - Naik, Aakash 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 - Kulla, Hannes T1 - In situ investigations of a mechanochemical cocrystal formation by X-ray diffraction in two different milling jars N2 - We present an in situ PXRD investigation of the mechanochemical cocrystal formation of pyrazinamide with pimelic acid in two milling jar materials. DFT calculations of the two synthesized polymorphs suggest that the relative stability is based on a conformation change of pyrazinamide in the cocrystal. T2 - 3. BAM-BfR Workshop CY - Berlin, Adlershof, Germany DA - 15.02.2018 KW - Mechanochemistry KW - Cocrystal KW - Polymorph KW - In situ XRD KW - DFT PY - 2018 AN - OPUS4-44315 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - GEN A1 - Laskina, Sofya T1 - Computing the forward and inverse problem of X-ray scattering N2 - Continuing progress in the field of X-ray scattering methods empowers scientists with new possibilities to capture the most important piece of information about the structure of the sample - its 3D electron density. Although the first methods appeared almost a century ago, recovering the density structure of a sample is still very problematic. Most avail-able imaging techniques transform a 3D electron density of a realspace structure into the 2D Fourier Transform of the intensity of scattered waves in the reciprocal space. This process causes a loss of information. Firstly, instead of a 3D sample, a 2D image is created, and secondly, the phase information of the scattered waves is lost. The latter is known as the ”phase problem” and poses a serious obstacle on a way to recover a 3D electron density. In this work, we draw attention to the problem of forward and inverse Small Angle X-Ray Scattering. In the first, forward, part, we rethink the existing pipelines to computationally simulate such scattering experiments. Although there are efficient implementations of fast Fourier transformation, they often have some drawbacks. For instance, to calculate a 3D fast Fourier transform it is required to place its density in the RAM. For high-resolution structures of size > 1024 3 , this becomes very problematic, as the whole density structure requires more than 16 GB of memory. CUDA solution allows for a very fast and parallelizable implementation of high-resolution data on hundreds of last-generation machines. Such computations are very pricy and inaccessible for most scientists. To bypass this limitation, we propose a solution for a split-up 3D fast Fourier transform, which is implemented as a sequence of 2D and 1D operations. We compare our implementation on the simulated 3D shapes and show the result of a proof-of-concept on 4096 3 Metallorganic framework density structure. In the second, inverse problem, we train an invertible neural network, that given scattering data can predict the shape and its parameters. The architecture is built such, that the inverse problem is learned together with the forward process - the Fourier Transformation. We achieved very good results with this architecture, nonetheless, further testing is required, as the current training set only encompasses three simple shapes: sphere, hard sphere and cylinder. All code to reproduce and analyze the results is available at https: //github.com/sofyalaski/SAXS-simulations. KW - Machine Learning KW - SAXS KW - DFT PY - 2023 SP - 1 EP - 76 PB - Freie Universität Berlin CY - Berlin AN - OPUS4-56798 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Michalchuk, Adam T1 - The Mechanochemical Excitation of Crystalline LiN3 N2 - Mechanochemical reactions are driven by the direct absorption of mechanical energy by a solid (often crystalline) material. Understanding how this energy is absorbed and ultimately causes a chemical transformation is essential for understanding the elementary stages of mechanochemical transformations. Using as a model system the energetic material LiN3 we here consider how vibrational energy flows through the crystal structure. By considering the compression response of the crystalline material we identify the partitioning of energy into an initial vibrational excitation. Subsequent energy flow is based on concepts of phonon–phonon scattering, which we calculate within a quasi-equilibrium model facilitated by phonon scattering data obtained from Density Functional Theory (DFT). Using this model we demonstrate how the moments (picoseconds) immediately following mechanical impact lead to significant thermal excitation of crystalline LiN3, sufficient to drive marked changes in its electronic structure and hence chemical reactivity. This work paves the way towards an ab initio approach to studying elementary processes in mechanochemical reactions involving crystalline solids. KW - Energetic materials KW - Ab initio simulation KW - DFT KW - Mechanochemistry PY - 2022 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-559310 DO - https://doi.org/10.1039/d2fd00112h SP - 1 EP - 20 PB - Royal Society of Chemistry AN - OPUS4-55931 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Bernges, T. A1 - Hanus, R. A1 - Wankmiller, B. A1 - Imasato, K. A1 - Lin, S. A1 - Ghidiu, M. A1 - Gerlitz, M. A1 - Peterlechner, M. A1 - Graham, S. A1 - Hautier, G. A1 - Pei, Y. A1 - Hansen, M. R. A1 - Wilde, G. A1 - Snyder, G. J. A1 - George, Janine A1 - Agne, M T. A1 - Zeier, W. G. T1 - Considering the Role of Ion Transport in Diffuson-Dominated Thermal Conductivity N2 - Next-generation thermal management requires the development of low lattice thermal conductivity materials, as observed in ionic conductors. For example, thermoelectric efficiency is increased when thermal conductivity is decreased. Detrimentally, high ionic conductivity leads to thermoelectric device degradation. Battery safety and design also require an understanding of thermal transport in ionic conductors. Ion mobility, structural complexity, and anharmonicity have been used to explain the thermal transport properties of ionic conductors. However, thermal and ionic transport are rarely discussed in direct comparison. Herein, the ionic conductivity of Ag+ argyrodites is found to change by orders of magnitude without altering the thermal conductivity. Thermal conductivity measurements and two-channel lattice dynamics modeling reveal that the majority of Ag+ vibrations have a non-propagating diffuson-like character, similar to amorphous materials. It is found that high ionic mobility is not a requirement for diffuson-mediated transport. Instead, the same bonding and structural traits that can lead to fast ionic conduction also lead to diffuson-mediated transport. Bridging the fields of solid-state ionics and thermal transport, it is proposed that a vibrational perspective can lead to new design strategies for functional ionic conducting materials. As a first step, the authors relate the so-called Meyer-Neldel behavior in ionic conductors to phonon occupations. KW - Diffusons KW - DFT KW - Phonons KW - Thermoelectrics PY - 2022 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-547140 DO - https://doi.org/10.1002/aenm.202200717 SN - 1614-6832 VL - 12 IS - 22 SP - 1 EP - 13 PB - Wiley VHC-Verlag AN - OPUS4-54714 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Arnold, M. A1 - Katzmann, J. A1 - Naik, Aakash A1 - Görne, A. L. A1 - Härtling, Thomas A1 - George, Janine A1 - Schuster, C. T1 - 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 - A recent approach to measure electron radiation doses in the kGy range is the use of phosphors with an irradiation dose-dependent luminescence decay time. However, the applicability of the previously investigated material NaYF4:Yb3+,Er3+ is limited as it shows pronounced fading. Therefore, in this work, a modified SrF2 synthesis is presented that results in SrF2 nanoparticles codoped with Yb and either Er, Hm, or Tm. To assess their suitability as dosimeter material, dose response, as well as its degree of fading over 50 up to 140 days after irradiation were measured. Fading rates as small as 5% in SrF2:Er,Yb and 4% in SrF2:Ho,Yb were derived, which are comparable to established dosimeter materials. A combination of spectroscopy, diffraction and DFT calculations was used to elucidate the effect of irradiation, pointing towards the formation of a secondary phase of Yb2+ that we predict could be Yb2OF2. This irreversible formation of a secondary phase is considered to be the explanation for the low fading behavior in SrF2-based phosphors compared to NaYF4:Yb, Er, a highly attractive feature for electron beam dosimetry. KW - DFT KW - Structure prediction PY - 2022 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-554115 DO - https://doi.org/10.1039/D2TC01773C SN - 2050-7526 VL - 10 IS - 32 SP - 11579 EP - 11587 PB - RSC CY - London AN - OPUS4-55411 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - INPR A1 - Liu, Yuanbin A1 - Morrow, Joe D. A1 - Ertural, Christina A1 - Fragapane, Natascia L. A1 - Gardner, John L. A. A1 - Naik, Aakash A1 - Zhou, Yuxing A1 - George, Janine A1 - Deringer, Volker L. T1 - An automated framework for exploring and learning potential-energy surfaces N2 - Machine learning has become ubiquitous in materials modelling and now routinely enables large-scale atomistic simulations with quantum-mechanical accuracy. However, developing machine-learned interatomic potentials requires high-quality training data, and the manual generation and curation of such data can be a major bottleneck. Here, we introduce an automated framework for the exploration and fitting of potential-energy surfaces, implemented in an openly available software package that we call autoplex ('automatic potential-landscape explorer'). We discuss design choices, particularly the interoperability with existing software architectures, and the ability for the end user to easily use the computational workflows provided. We show wide-ranging capability demonstrations: for the titanium-oxygen system, SiO2, crystalline and liquid water, as well as phase-change memory materials. More generally, our study illustrates how automation can speed up atomistic machine learning -- with a long-term vision of making it a genuine mainstream tool in physics, chemistry, and materials science. KW - Machine learned interatomic potentials KW - Ab initio KW - DFT KW - Automation KW - Workflows KW - Machine learning KW - Materials design KW - Materials discovery PY - 2024 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-623188 DO - https://doi.org/10.48550/arXiv.2412.16736 SN - 2331-8422 SP - 1 EP - 27 PB - Cornell University CY - Ithaca, NY AN - OPUS4-62318 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - INPR A1 - Ganose, Alex A1 - Sahasrabuddhe, Hrushikesh A1 - Asta, Mark A1 - Beck, Kevin A1 - Biswas, Tathagata A1 - Bonkowski, Alexander A1 - Bustamante, Joana A1 - Chen, Xin A1 - Chiang, Yuan A1 - Chrzan, Daryl A1 - Clary, Jacob A1 - Cohen, Orion A1 - Ertural, Christina A1 - George, Janine A1 - Gallant, Max A1 - George, Janine A1 - Gerits, Sophie A1 - Goodall, Rhys A1 - Guha, Rishabh A1 - Hautier, Geoffroy A1 - Horton, Matthew A1 - Kaplan, Aaron A1 - Kingsbury, Ryan A1 - Kuner, Matthew A1 - Li, Bryant A1 - Linn, Xavier A1 - McDermott, Matthew A1 - Rohith Srinivaas Mohanakrishnan, A1 - Naik, Aakash A1 - Neaton, Jeffrey A1 - Persson, Kristin A1 - Petretto, Guido A1 - Purcell, Thomas A1 - Ricci, Francesco A1 - Rich, Benjamin A1 - Riebesell, Janosh A1 - Rignanese, Gian-Marco A1 - Rosen, Andrew A1 - Scheffler, Matthias A1 - Schmidt, Jonathan A1 - Shen, Jimmy-Xuan A1 - Sobolev, Andrei A1 - Sundararaman, Ravishankar A1 - Tezak, Cooper A1 - Trinquet, Victor A1 - Varley, Joel A1 - Vigil-Fowler, Derek A1 - Wang, Duo A1 - Waroquiers, David A1 - Wen, Mingjian A1 - Yang, Han A1 - Zheng, Hui A1 - Zheng, Jiongzhi A1 - Zhu, Zhuoying A1 - Jain, Anubhav T1 - Atomate2: Modular workflows for materials science N2 - High-throughput density functional theory (DFT) calculations have become a vital element of computational materials science, enabling materials screening, property database generation, and training of “universal” machine learning models. While several software frameworks have emerged to support these computational efforts, new developments such as machine learned force fields have increased demands for more flexible and programmable workflow solutions. This manuscript introduces atomate2, a comprehensive evolution of our original atomate framework, designed to address existing limitations in computational materials research infrastructure. Key features include the support for multiple electronic structure packages and interoperability between them, along with generalizable workflows that can be written in an abstract form irrespective of the DFT package or machine learning force field used within them. Our hope is that atomate2’s improved usability and extensibility can reduce technical barriers for high-throughput research workflows and facilitate the rapid adoption of emerging methods in computational material science. KW - Automation KW - DFT KW - Digitalisation KW - Materials design KW - Machine learning KW - Machine learned interatomic potentials PY - 2025 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-624487 DO - https://doi.org/10.26434/chemrxiv-2025-tcr5h SN - 2573-2293 SP - 1 EP - 66 PB - American Chemical Society (ACS) CY - Washington, D.C. AN - OPUS4-62448 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Bustamante Pineda, Joana Cecibel T1 - Lattice thermal conductivity on argyrodite compounds Ag8TS6 (T= Si, Ge and Sn): Experimental and full ab initio approach N2 - Thermoelectric materials offer a promising solution for energy conservation and environmental protection by addressing the significant heat waste in energy production. These innovative materials have the ability to directly convert heat into electricity and vice versa. Here, we have studied the family of Ag8TS6 (T= Sn, Ge, S) by combining experimental and theoretical approaches. We investigated their structural, vibrational and thermodynamic properties employing the Local Orbital Basis Suite Towards Electronic-Structure Reconstruction (LOBSTER), density-functional theory (DFT) and lattice dynamics computations included in the automated workflows in atomate2 software. These structures exhibit lower group and sound velocities, along with higher Grüneisen parameters, primarily influenced by the vibrations of Ag⁺ ions due to their low bonding interaction. Based on a newly introduced two-channel lattice dynamics model[4] including lifetimes stated from Grüneisen parameters (similar to the approach in the Slack model), we were also able to successfully model lattice thermal conductivity and compare these results with experimental measurements. Finally, a bonding analysis of all studied compounds was carried out, indicating a similar bonding situation in all our structures. By using bonding analysis and our model for the lattice thermal conductivity calculations, we demonstrate that the bond heterogeneity found in Ag8SnS6, Ag8GeS6, and Ag8SiS6 is closely connected to its low thermal conductivity, dominated mainly by heat transport via a diffusive exchange of thermal energy between phonon modes (diffusons). T2 - XLVIII International Congress of Theoretical Chemists of Latin Expression CY - Cartagena, Colombia DA - 14.07.2025 KW - Grüneisen parameter KW - LOBSTER KW - DFT KW - QHA PY - 2025 AN - OPUS4-63818 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - George, Janine T1 - Introduction to computational methods with periodic boundaries N2 - I provided an introduction to density functional theory (DFT) with periodic boundary conditions. I repeated the derivation of DFT and the Bloch theorem. Then, I combined both concepts. Additionally, I delivered an outlook to automatic DFT for solids and machine learning in the field. T2 - Computational Methods in Crystallography CY - Lausanne, Switzerland DA - 09.09.2025 KW - DFT KW - Automation KW - Machine Learning KW - Bloch Theorem KW - Materials Design PY - 2025 AN - OPUS4-64044 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -