Harness proprietary machine learning models trained on millions of peer-reviewed publications, experimental datasets, and simulation outputs to predict properties, screen candidates, and shorten discovery cycles by up to 70%.
From hypothesis to validated candidate in four streamlined steps.
Input desired mechanical, thermal, electronic, or chemical properties using natural language or structured parameters.
Our models scan 140+ languages, cross-referencing Aevum's encyclopedia, open databases, and proprietary simulation archives.
Graph neural networks and transformer models rank candidates by feasibility, stability, and synthesis pathways.
Export ranked lists, crystallographic files, and uncertainty metrics directly to VASP, LAMMPS, or Jupyter environments.
Built for materials scientists, chemical engineers, and computational researchers.
Combines text embeddings, crystallographic fingerprints, and spectroscopic data for holistic material characterization.
Every prediction includes confidence intervals and source traceability, critical for high-stakes R&D decisions.
Continuous ingestion of preprints, patents, and experimental datasets keeps models current without manual retraining.
Share projects, annotate predictions, and manage versioned material libraries with your research team.
Trusted by academic labs and enterprise R&D departments worldwide.
Screening fast-ion conductors with optimized ionic mobility and electrochemical stability windows.
Accelerating the design of high-performance, compostable alternatives to conventional plastics.
Predicting phase stability and creep resistance for next-generation turbine components.
Identifying stimuli-responsive materials for controlled release and targeted therapeutics.
Seamless deployment into existing computational workflows.
Hear from the scientists integrating Aevum into their discovery pipelines.
"Cut our initial screening phase from months to weeks. The uncertainty metrics are a game-changer for grant proposals and lab prioritization."
"The DFT export and Jupyter integration are flawless. We embedded it directly into our curriculum for computational materials science."
"Finally, an AI tool that respects academic rigor. Every prediction traces back to verified literature. Essential for our polymer R&D team."
Join hundreds of research institutions and industry labs. Free tier includes 500 predictions/month and full API access.
No credit card required. Institutional & enterprise licensing available.