xemx.space provides combinatorial thin-film synthesis and automated property characterization services, enabling researchers to map material properties across hundreds of compositions in a single campaign. The platform co-sputters up to seven elements onto a 100 mm wafer, creating 342 unique compositions that are automatically measured for phase, electrical, optical, or magnetic properties. Bayesian optimization guides subsequent campaigns toward the most informative composition regions, accelerating materials discovery.
Funding
Funding not disclosed
Founders
Product
Problem
Traditional materials discovery evaluates one composition at a time, a slow and resource-intensive process that becomes impractical when exploring multi-element systems. The composition space for advanced materials is often too vast to navigate sequentially, delaying the identification of optimal formulations for applications in catalysis, electronics, and structural materials.
Solution
xemx.space replaces sequential material evaluation with complete composition-property maps generated in a single campaign. The company co-sputters up to seven elements onto a 100 mm wafer, creating 342 unique physical thin-film compositions in one run. Each composition is automatically measured for relevant properties such as phase, electrical, optical, or magnetic characteristics. Bayesian optimization uses the resulting dataset to direct subsequent campaigns toward the most informative composition sub-spaces. Once target compositions are identified, the company produces controlled uniform depositions for downstream validation, prototyping, and scale-up.
Target Audience
Primary customers are materials researchers and R&D teams in academia and industry working on multi-element alloys, nitrides, and functional thin films for applications in catalysis, electronics, optics, and corrosion-resistant coatings.
Features
- Combinatorial co-sputtering of up to 7 elements onto a 100 mm wafer in a single run
- 342 unique physical thin-film compositions generated per campaign via continuous lateral composition gradients
- Automated characterization including XRD phase analysis, 4-point probe resistivity, UV-VIS reflectance spectroscopy, nanoindentation, MOKE magnetic property mapping, and Scanning Droplet Cell corrosion testing
- Bayesian optimization algorithms that direct multi-round campaigns toward the most informative composition regions
- Controlled uniform depositions of target compositions for validation, prototyping, and scale-up
- Published results across Ni-Pd-Pt-Ru and Co-Fe-Ni systems for activity-stability mapping, plus CrAlN and transition metal nitride systems