APPROACH

materialsIN uses advanced data analytic techniques and material science for the inspection, development, design, discovery and handling of materials.

materialsIN works with a wide range of data modalities, including spectra, imaging, textual, and numerical, to achieve automated and accurate insights into materials-related issues.

Our solutions are differentiated by their speed and accuracy, which save customers time and money.

Its first three products, materialsINspect, materialsINsight, and materialsIN Pro-Opt, can be applied to any material.

Our design-with-intent approach provides customers with the ability to select materials that meet multiple performance criteria, including advanced engineering features that meet the needs of emerging technologies.

The materialsIN method also allows for the simultaneous consideration of other factors, such as sustainability, toxicity, cost, and availability.

materialsIN VirtualFAB

materialsIN VirtualFAB brings data, materials science, and AI together in a proprietary computational environment to help organizations expedite the move from data to insight—and from insight to better-informed experimentation and innovation. As a result, materialsIN eliminates organizations’ costly experimental cycles and, simultaneously, accelerates discovery and technology insertion.

Embed synthesis and models directly within AI workflows

Current workflows rely on partial manual integration of physics-based modeling with AI methods, limiting throughput, reproducibility, and scalability.

materialsIN VirtualFAB addresses this core limitation with a physics-informed AI capability that embeds synthesis and process models directly within AI-driven workflows. This integration enables automated, manufacturability-aware evaluation of candidate materials and process pathways, reducing dependence on manual analysis and enabling rapid screening under fabrication-relevant constraints.

Support iterative R&D cycles

materialsIN VirtualFAB transforms materials R&D from a largely sequential process of experimentation and analysis into an iterative cycle of prediction, virtual exploration, targeted experimentation, and learning.

Integrate data → Learn relationships → Predict outcomes → Explore alternatives → Optimize solutions → Recommend experiments → Validate experimentally → Learn from new results

Enable more discovery and innovation

materialsIN VirtualFAB accomplishes the following for its customers:

• • Integrate complex materials, process, experimental, and manufacturing data;
  • Discover relationships among processing, structure, properties, and performance;
  • Develop predictive models from experimental and industrial data;
  • Explore materials and process conditions virtually;
  • Identify promising regions of complex design spaces;
  • Optimize materials, formulations, and processing conditions against defined objectives;
  • Recommend high-value experiments and development pathways;
  • Incorporate new experimental results to continuously improve models and recommendations; and
  • Reduce unnecessary experimentation and accelerate learning and innovation.