Evidence | xemX

Figures

Research figures used across application pages.

Combinatorial thin-film workflow for materials-library studies. — Combinatorial thin-film workflow
A material library connects synthesis, high-throughput characterization, data handling, and candidate selection.Ludwig, npj Comput. Mater. 2019, Fig. 1

High-throughput characterization methods for thin-film material libraries. — Library-scale characterization
Composition, structure, magnetic, electrical, optical, mechanical, and microstructure measurements feed measured maps.Ludwig, npj Comput. Mater. 2019, Fig. 2

Processing-library workflow for Al-Cu-O thin-film samples. — Processing-library samples
Deposition conditions, composition, and SEM microstructure are tied to measured library positions.Banko et al., Commun. Mater. 2020, Fig. 1

Microstructure classification across composition and deposition temperature. — Microstructure maps
Image data are organized across composition and process coordinates for microstructure classification.Banko et al., Commun. Mater. 2020, Fig. 2

Structure-zone maps across aluminum content and deposition temperature. — Structure-zone diagram
Measured and predicted microstructure classes define process ranges for thin-film samples.Banko et al., Commun. Mater. 2020, Fig. 6

Variational autoencoder workflow and latent-space plots for XRD patterns. — XRD latent-space analysis
Large diffraction datasets are organized by phase similarity and structure signals before regions are selected.Banko et al., npj Comput. Mater. 2021, Fig. 1

XRD dataset visualization and latent-space grouping. — XRD dataset visualization
Library-scale diffraction data are grouped to compare phase behavior across measured thin-film samples.Banko et al., npj Comput. Mater. 2021, Fig. 2

References

Cited sources.

Thelen, F.; Zehl, R.; Zerdoumi, R.; Bürgel, J. L.; Banko, L.; Schuhmann, W.; Ludwig, A. Accelerating Combinatorial Electrocatalyst Discovery with Bayesian Optimization: A Case Study in the Quaternary System Ni-Pd-Pt-Ru for the Oxygen Evolution Reaction. Adv. Sci. 2025, 12 (35), e07302.

Current example of library fabrication, high-throughput characterization, Bayesian optimization, and follow-up OER testing.

Banko, L.; Krysiak, O. A.; Pedersen, J. K.; Xiao, B.; Savan, A.; Löffler, T.; Baha, S.; Rossmeisl, J.; Schuhmann, W.; Ludwig, A. Unravelling Composition-Activity-Stability Trends in High Entropy Alloy Electrocatalysts by Using a Data-Guided Combinatorial Synthesis Strategy and Computational Modeling. Adv. Energy Mater. 2022, 12, 2103312.

Composition, activity, and stability mapping across high-entropy alloy electrocatalyst libraries.

Banko, L.; Tetteh, E. B.; Kostka, A.; Piotrowiak, T. H.; Krysiak, O. A.; Hagemann, U.; Andronescu, C.; Schuhmann, W.; Ludwig, A. Microscale Combinatorial Libraries for the Discovery of High-Entropy Materials. Adv. Mater. 2023, 35, 2207635.

Microscale material libraries for dense sampling of high-entropy material composition spaces.

Ludwig, A. Discovery of New Materials Using Combinatorial Synthesis and High-Throughput Characterization of Thin-Film Materials Libraries Combined with Computational Methods. npj Comput. Mater. 2019, 5, 70.

Core thin-film materials-library platform, high-throughput characterization, and composition-property mapping.

Banko, L.; Lysogorskiy, Y.; Grochla, D.; Naujoks, D.; Drautz, R.; Ludwig, A. Predicting Structure Zone Diagrams for Thin Film Synthesis by Generative Machine Learning. Commun. Mater. 2020, 1, 15.

Processing libraries and machine-learning structure-zone diagrams for thin-film microstructure selection.

Banko, L.; Maffettone, P. M.; Naujoks, D.; Olds, D.; Ludwig, A. Deep Learning for Visualization and Novelty Detection in Large X-ray Diffraction Datasets. npj Comput. Mater. 2021, 7, 104.

Machine-learning support for interpreting large XRD datasets from thin-film library measurements.

Banko, L.; Ries, S.; Grochla, D.; Arghavani, M.; Salomon, S.; Pfetzing-Micklich, J.; Kostka, A.; Rogalla, D.; Schulze, J.; Awakowicz, P.; Ludwig, A. Effects of the Ion to Growth Flux Ratio on the Constitution and Mechanical Properties of Cr_1-x-Al_x-N Thin Films. ACS Comb. Sci. 2019, 21 (12), 782-793.

Reactive sputtering, automated XRD, plasma diagnostics, hardness, and elastic modulus in Cr-Al-N thin-film libraries.