Kebotix
Accelerating materials discovery through the world's first self-driving laboratory ecosystem.

Accelerating materials discovery through high-throughput density functional theory and automated thermodynamic analysis.
The Open Quantum Materials Database (OQMD) is a high-throughput database of thermodynamic and structural properties of materials, calculated using density functional theory (DFT). Developed by the Wolverton Group at Northwestern University, the platform provides a rigorous technical architecture for the computational design of new materials. As of 2026, OQMD remains a cornerstone in the materials informatics ecosystem, hosting over 1.2 million compounds. Its core utility lies in the systematic calculation of formation energies, which enables the construction of multi-element phase diagrams and the identification of ground-state structures. The database leverages the Vienna Ab initio Simulation Package (VASP) for its calculations, ensuring high fidelity for both known experimental structures and predicted hypothetical compounds. For the 2026 market, OQMD serves as a critical training ground for generative AI models and Graph Neural Networks (GNNs) seeking to predict material stability without the computational overhead of ab initio methods. By providing a standardized RESTful API and the qmpy Python library, it facilitates seamless integration into automated research pipelines for battery design, catalysis, and structural alloys.
Automated generation of multi-dimensional convex hulls to determine the thermodynamic stability of any material relative to its elements.
Verified feedback from the global deployment network.
Post queries, share implementation strategies, and help other users.
Seamless export of optimized parameters for VASP, including K-point grids and pseudopotential configurations.
Calculates stability under varying chemical potentials (e.g., oxygen partial pressure).
Native support for exporting datasets directly into GNN architectures like MegNet or CGCNN.
Support for the Open Databases Integration for Materials Design standard.
Identifies material prototypes by mapping unknown structures to known crystal symmetries.
Provides both GGA and meta-GGA (SCAN) level band gap estimates for electronic properties.
Identifying high-voltage, stable cathode materials for Li-ion batteries.
Registry Updated:2/7/2026
Finding materials with low thermal conductivity and high power factor.
Predicting surface activity of alloys for hydrogen evolution.