Next generation materials, designed using AI

We use purpose-built, physics-informed AI models to predict optimal alloy compositions and properties with high precision and speed. Our models are trained on extensive materials datasets, enabling them to identify and model complex relationships between materials, processes, structures, and properties. These AI models work in tandem with multi-objective optimizers, balancing conflicting design objectives to achieve the best possible outcomes. This approach drastically reduces development time and costs, enabling the rapid design and optimization of high-performing, reliable materials for industrial applications.

We use multi-scale simulations to evaluate new alloys comprehensively. By integrating simulations at varying size-scales, such as nano, micro, meso, and macro scales, we understand how interactions at the smallest scales drive real-world performance. This approach allows us to predict performance under various conditions, ensuring materials meet specific criteria and reducing the need for extensive experimental testing.

In the materials science sector, data is often scarce. To address this, we generate our own hybrid datasets through a combination of experiments and simulations. This approach yields more consistent, higher-quality data across a diverse range of scenarios. As a result, our models, trained on this proprietary data, demonstrate superior performance and reliability.