TY - CONF A1 - Bayerlein, Bernd T1 - Innovations- Plattform Material Digital N2 - Mit der Initiative MaterialDigital fördert das BMBF seit dem letzten Jahr ein wichtiges Instrument zur Digitalisierung der Materialwissenschaft und Werkstofftechnik in Deutschland. In der ersten Phase dieser Initiative wird seit Juli 2019 die Plattform MaterialDigital von einem Konsortium aufgestellt. Ziel dieser Plattform ist der Aufbau eines virtuellen Materialdatenraums, um zusammen mit allen Interessenten die Systematisierung des Umgangs mit Werkstoffdaten voranzutreiben. In einer zweiten Phase werden akademische F&E-Projekte voraussichtlich Anfang 2021 gefördert. In jedem dieser Projekte wird an einer konkreten Fragestellung das Themenfeld „Digitalisierung der Materialwissenschaft und Werkstofftechnik“ adressiert und multidisziplinär bearbeitet. In einer dritten Phase werden im Rahmen industriegeführter vorwettbewerblicher Verbundprojekte mit der gleichen Zielsetzung gefördert. T2 - Workshop - “Datenmanagement in der Additiven Fertigung” CY - Online meeting DA - 27.10.2020 KW - Digitalisierung KW - Plattform Material Digital KW - Virtueller Materialdatenraum KW - Vernetzung KW - Ontologien PY - 2020 AN - OPUS4-51609 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - GEN A1 - Naik, Aakash A1 - Ertural, Christina A1 - Dhamrait, Nidal A1 - Benner, Philipp 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 - JOUR A1 - Valdestilhas, Andre A1 - Bayerlein, Bernd A1 - Moreno Torres, Benjami A1 - Zia, Ghezal Ahmad Jan A1 - Muth, Thilo T1 - The Intersection Between Semantic Web and Materials Science N2 - The application and benefits of Semantic Web Technologies (SWT) for managing, sharing, and (re-)using of research data are demonstrated in implementations in the field of Materials Science and Engineering (MSE). However, a compilation and classification are needed to fully recognize the scattered published works with its unique added values. Here, the primary use of SWT at the interface with MSE is identified using specifically created categories. This overview highlights promising opportunities for the application of SWT to MSE, such as enhancing the quality of experimental processes, enriching data with contextual information in knowledge graphs, or using ontologies to perform specific queries on semantically structured data. While interdisciplinary work between the two fields is still in its early stages, a great need is identified to facilitate access for nonexperts and develop and provide user-friendly tools and workflows. The full potential of SWT can best be achieved in the long term by the broad acceptance and active participation of the MSE community. In perspective, these technological solutions will advance the field of MSE by making data FAIR. Data-driven approaches will benefit from these data structures and their connections to catalyze knowledge generation in MSE. KW - Linked open data KW - Materials science KW - Ontology KW - Semantic web PY - 2023 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-575506 DO - https://doi.org/10.1002/aisy.202300051 PB - Wiley-VCH GmbH CY - Weinheim AN - OPUS4-57550 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 - JOUR A1 - Weichert, Dorina A1 - Kister, Alexander A1 - Volbach, Peter A1 - Houben, Sebastian A1 - Trost, Marcus A1 - Wrobel, Stefan T1 - Explainable production planning under partial observability in high-precision manufacturing N2 - Conceptually, high-precision manufacturing is a sequence of production and measurement steps, where both kinds of steps require to use non-deterministic models to represent production and measurement tolerances. This paper demonstrates how to effectively represent these manufacturing processes as Partially Observable Markov Decision Processes (POMDP) and derive an offline strategy with state-of-the-art Monte Carlo Tree Search (MCTS) approaches. In doing so, we face two challenges: a continuous observation space and explainability requirements from the side of the process engineers. As a result, we find that a tradeoff between the quantitative performance of the solution and its explainability is required. In a nutshell, the paper elucidates the entire process of explainable production planning: We design and validate a white-box simulation from expert knowledge, examine state-of-the-art POMDP solvers, and discuss our results from both the perspective of machine learning research and as an illustration for high-precision manufacturing practitioners. KW - Explainability KW - Manufacturing KW - Reinforcement Learning KW - Monte Carlo tree search KW - Partially observable Markov decision process PY - 2023 DO - https://doi.org/10.1016/j.jmsy.2023.08.009 SN - 0278-6125 VL - 70 SP - 514 EP - 524 PB - Elsevier Ltd. CY - Southfield AN - OPUS4-58963 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Weichert, Dorina A1 - Kister, Alexander A1 - Houben, Sebastian A1 - Link, Patrick A1 - Ernis, Gunar ED - Kiyavash, Negar ED - Mooij, Joris M. T1 - Robust Entropy Search for Safe Efficient Bayesian Optimization N2 - The practical use of Bayesian Optimization (BO) in engineering applications imposes special requirements: high sampling efficiency on the one hand and finding a robust solution on the other hand. We address the case of adversarial robustness, where all parameters are controllable during the optimization process, but a subset of them is uncontrollable or even adversely perturbed at the time of application. To this end, we develop an efficient information-based acquisition function that we call Robust Entropy Search (RES). We empirically demonstrate its benefits in experiments on synthetic and real-life data. The results show that RES reliably finds robust optima, outperforming state-of-the-art algorithms. T2 - UAI 2024 CY - Barcelona, Spain DA - 15.07.2024 KW - Bayesian Optimization KW - Gaussian process KW - Active learning PY - 2024 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-613523 UR - https://proceedings.mlr.press/v244/weichert24a.html SN - 2640-3498 VL - 244 SP - 3711 EP - 3729 PB - Proceedings of Machine Learning Research AN - OPUS4-61352 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Paul, Nathalie A1 - Kister, Alexander A1 - Schnellhardt, Thorben A1 - Fetz, Maximilian A1 - Hecker, Dirk A1 - Wirtz, Tim ED - Meo, Rosa ED - Silvestri, Fabrizio T1 - Reinforcement Learning for Segmented Manufacturing N2 - The manufacturing of large components is, in comparison to small components, cost intensive. This is due to the sheer size of the components and the limited scalability in number of produced items. To take advantage of the effects of small component production we segment the large components into smaller parts and schedule the production of these parts on regular-sized machine tools. We propose to apply and adapt recent developments in reinforcement learning in combination with heuristics to efficiently solve the resulting segmentation and assignment problem. In particular, we solve the assignment problem up to a factor of 8 faster and only a few percentages less accurate than a classic solver from operations research. T2 - European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases 2023 CY - Turin, Italy DA - 18.09.2023 KW - Reinforcement Learning KW - Assignment Problem KW - Large component manufacturing PY - 2025 DO - https://doi.org/10.1007/978-3-031-74640-6_38 VL - 1 IS - 1 SP - 470 EP - 485 PB - Springer Cham AN - OPUS4-63031 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - INPR A1 - Alphonse, Amal A1 - Hintermüller, Michael A1 - Kister, Alexander A1 - Lun, Chin Hang A1 - Sirotenko, Clemens T1 - A neural network approach to learning solutions of a class of elliptic variational inequalities N2 - We develop a weak adversarial approach to solving obstacle problems using neural networks. By employing (generalised) regularised gap functions and their properties we rewrite the obstacle problem (which is an elliptic variational inequality) as a minmax problem, providing a natural formulation amenable to learning. Our approach, in contrast to much of the literature, does not require the elliptic operator to be symmetric. We provide an error analysis for suitable discretisations of the continuous problem, estimating in particular the approximation and statistical errors. Parametrising the solution and test function as neural networks, we apply a modified gradient descent ascent algorithm to treat the problem and conclude the paper with various examples and experiments. Our solution algorithm is in particular able to easily handle obstacle problems that feature biactivity (or lack of strict complementarity), a situation that poses difficulty for traditional numerical methods. KW - Obstacle Problems KW - Numerical Analysis KW - Optimization and Control KW - Simulation PY - 2024 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-622160 DO - https://doi.org/10.48550/arXiv.2411.18565 SP - 1 EP - 38 AN - OPUS4-62216 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - GEN A1 - Maack, Stefan A1 - Benner, Philipp A1 - Kröll, Mirco A1 - Prager, Jens A1 - Daum, Werner A1 - Casperson, Ralf A1 - Heckel, Thomas A1 - Spaltmann, Dirk A1 - et al., ED - Wahlster, W. ED - Winterhalter, C. T1 - Deutsche Normungsroadmap künstliche Intelligenz T1 - German standardization roadmap on artificial intelligence N2 - Im Auftrag des Bundesministeriums für Wirtschaft und Klimaschutz haben DIN und DKE im Januar 2022 die Arbeiten an der zweiten Ausgabe der Deutschen Normungsroadmap Künstliche Intelligenz gestartet. In einem breiten Beteiligungsprozess und unter Mitwirkung von mehr als 570 Fachleuten aus Wirtschaft, Wissenschaft, öffentlicher Hand und Zivilgesellschaft wurde damit der strategische Fahrplan für die KI-Normung weiterentwickelt. Koordiniert und begleitet wurden diese Arbeiten von einer hochrangigen Koordinierungsgruppe für KI-Normung und -Konformität. Mit der Normungsroadmap wird eine Maßnahme der KI-Strategie der Bundesregierung umgesetzt und damit ein wesentlicher Beitrag zur „KI – Made in Germany“ geleistet. Die Normung ist Teil der KI-Strategie und ein strategisches Instrument zur Stärkung der Innovations- und Wettbewerbsfähigkeit der deutschen und europäischen Wirtschaft. Nicht zuletzt deshalb spielt sie im geplanten europäischen Rechtsrahmen für KI, dem Artificial Intelligence Act, eine besondere Rolle. N2 - On behalf of the German Federal Ministry of Economic Affairs and Climate Action, DIN and DKE started work on the second edition of the German Standardization Roadmap Artificial Intelligence in January 2022. With the broad participation and involvement of more than 570 experts from industry, science, the public sector and civil society, the strategic Roadmap for AI standardization was thus further developed. This work was coordinated and accompanied by a high-level coordination group for AI standardization and conformity. The standardization roadmap implements a measure of the German government’s AI Strategy and thus makes a significant contribution to “AI – Made in Germany”. Standardization is part of the AI Strategy and is a strategic instrument for strengthening the innovation and competitiveness of the German and European economies. Not least for this reason, standardization plays a special role in the planned European legal framework for AI, the Artificial Intelligence Act. KW - Digitalisierung KW - Künstliche Intelligenz KW - Datenwissenschaft KW - Normung Roadmap KW - Data science KW - Artificial intelligence KW - Standardization roadmap KW - Digitization PY - 2022 UR - https://www.din.de/resource/blob/1072988/20bf33d405710a703aa26f81362493bb/ausgabe-2-nrm-ki-data.pdf UR - https://www.dke.de/de/arbeitsfelder/core-safety/normungsroadmap-ki SP - 1 EP - 448 PB - Deutsches Institut für Normung (DIN) CY - Berlin ET - 2. AN - OPUS4-56576 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Weichert, D. T1 - Robustness in Fatigue Strength Estimation N2 - Fatigue strength estimation is a costly manual material characterization process in which state-of-the-art approaches follow a standardized experiment and analysis procedure. In this paper, we examine a modular, Machine Learning-based approach for fatigue strength estimation that is likely to reduce the number of experiments and, thus, the overall experimental costs. Despite its high potential, deployment of a new approach in a real-life lab requires more than the theoretical definition and simulation. Therefore, we study the robustness of the approach against misspecification of the prior and discretization of the specified loads. We identify its applicability and its advantageous behavior over the state-of-the-art methods, potentially reducing the number of costly experiment. T2 - 2nd Annual AAAI Workshop on AI to Accelerate Science and Engineering CY - Washington DC, USA DA - 07.02.2023 KW - Active Learning KW - Gaussian Process KW - Fatigue Strength PY - 2022 AN - OPUS4-56684 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -