Kebotix
Accelerating materials discovery through the world's first self-driving laboratory ecosystem.
Accelerate materials discovery with a unified Materials Informatics platform and AI-driven R&D.
Materials Zone is a leading Materials Informatics (MI) platform designed to catalyze the digital transformation of materials and chemicals R&D. By 2026, the platform has emerged as a critical infrastructure for industries ranging from energy storage and semiconductors to polymers and coatings. Its architecture centers on a 'Materials Digital Twin,' which harmonizes data from disparate sources including experimental lab equipment, simulation tools, and legacy spreadsheets. Unlike generic data science platforms, Materials Zone employs specialized AI models optimized for the high-dimensional, small-dataset environments typical of materials research. The platform facilitates the entire R&D lifecycle: from automated data harvesting and normalization to predictive modeling and Design of Experiments (DoE). Its primary value proposition lies in its ability to significantly reduce time-to-market by minimizing trial-and-error cycles and enabling cross-functional teams to collaborate on a single, validated source of truth. As of 2026, its technical ecosystem supports seamless integration with laboratory automation and advanced manufacturing workflows, providing real-time insights that align experimental results with commercial performance requirements.
Creates a comprehensive digital representation of every material iteration, including processing parameters and chemical structure.
Accelerating materials discovery through the world's first self-driving laboratory ecosystem.
The OS for AI-driven materials discovery and manufacturing optimization.
The industry-standard open-source library for data mining and feature engineering in materials science.
Accelerating materials discovery through physics-aware generative AI and inverse design.
Verified feedback from the global deployment network.
Post queries, share implementation strategies, and help other users.
Uses Bayesian optimization to suggest the next best experiment, maximizing information gain with minimal trials.
High-speed rendering of complex materials datasets in multi-axis charts for pattern recognition.
Middleware that monitors laboratory instruments and automatically pushes new data to the cloud.
Proprietary algorithms specifically tuned for sparse and noisy scientific data.
Connects internal R&D data with external scientific literature and chemical databases.
Analyzes how specific manufacturing steps (e.g., sintering temperature) affect final performance characteristics.
Researchers struggle to optimize electrolyte conductivity while maintaining thermal stability.
Registry Updated:2/7/2026
Iterate until target performance is reached.
Balancing UV resistance and mechanical strength in new biodegradable plastics.
Identifying correlations between deposition parameters and crystal defects.