01
Complex alloys
Map multicomponent alloy regions before larger builds.
We test alloy and high-entropy material libraries to measure phase, structure, and target properties before bulk or part-level tests.
Platform Characteristics
Deposition-to-characterization path.
02
Physical sample library
Create a real composition-spread thin-film library with 342 registered measurement positions.03
Composition map
Map element ratios by EDX/EDS or WDX for the material system.04
Structure and properties
Measure XRD phase data and selected electrical, mechanical, optical, magnetic, or electrochemical response.05
Scoped follow-up
Scanning droplet cell (SDC), SECCM, XPS, microscopy, or interface analysis can be added when surface change or a localized measurement decides the next step.06
Next experiment
Measured maps, Bayesian optimization, or Gaussian-process selection support repeat samples or a narrower campaign.Material decision
Where this applies.
Relevant areas
Relevant systems include high-entropy alloys, complex solid solutions, shape-memory directions, and multicomponent alloy screens.
Experimental plan
Deposit a multielement thin-film library, measure composition and structure, add the property screen tied to the decision, and repeat selected regions in the needed format.
Examples
- High-entropy alloys
- Complex solid solutions
- Shape-memory alloy directions
- Multicomponent alloy composition screens
Methods used
- multisource co-sputtering
- composition-spread libraries
- XRD mapping
- nanoindentation
- four-point probe
- localized functional screening
Measurements
- composition
- phase
- microstructure
- hardness or modulus
- electrical response
- magnetic or electrochemical response
Outputs
- alloy composition maps
- candidate regions
- excluded phase regions
- follow-up samples
What comes back: Measured alloy composition ranges for bulk alloy, powder, part-level, or final-format tests.
Figures
Alloy-library and microstructure figures.
Combinatorial thin-film workflow
A material library connects synthesis, high-throughput characterization, data handling, and candidate selection.Ludwig, npj Comput. Mater. 2019, Fig. 1
Microstructure maps
Image data are organized across composition and process coordinates for microstructure classification.Banko et al., Commun. Mater. 2020, Fig. 2Closest Evidence
Closest published alloy demonstrations.
Banko et al., Adv. Mater. 2023
Microscale high-entropy libraries
Dense local libraries provide a method for comparing many high-entropy compositions.Open sourceBanko et al., Commun. Mater. 2020
Process-microstructure maps
Processing libraries connect deposition conditions, composition, and microstructure.Open sourceBanko et al., Adv. Energy Mater. 2022
High-entropy composition-property trends
Composition-property mapping was used to compare high-entropy material regions.Open sourcePlatform Basis
Methods behind the screen.
Ludwig, npj Comput. Mater. 2019
Combinatorial thin-film synthesis, high-throughput characterization, data handling, and composition-property mapping.Open sourceBanko et al., npj Comput. Mater. 2021
Deep-learning visualization and novelty detection for large XRD datasets from thin-film measurements.Open sourceBanko and Ludwig, ACS Comb. Sci. 2020
Experimental materials data management for linked samples, metadata, and analysis workflows.Open sourceReferences
Cited sources.
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 combinatorial libraries for high-entropy materials and denser local composition coverage.
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. 1, 15 (2020).
Processing libraries and generative machine learning used to select thin-film microstructure ranges.
Banko, L.; Krysiak, O. A.; Pedersen, J. K.; Xiao, B.; Savan, A.; Loffler, 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 trends in high-entropy alloy electrocatalyst libraries.
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. 5, 70 (2019).
Combinatorial thin-film synthesis, high-throughput characterization, data handling, and composition-property mapping.
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. 7, 104 (2021).
Deep-learning visualization and novelty detection for large XRD datasets from thin-film measurements.
Banko, L.; Ludwig, A. Fast-Track to Research Data Management in Experimental Material Science-Setting the Ground for Research Group Level Materials Digitalization. ACS Comb. Sci. 2020, 22 (8), 401-409.
Experimental materials data management for linked samples, metadata, and analysis workflows.