A deeper view of the materials frontier.
DISCOVIUM is an autonomous, physics-first discovery engine for materials that must stay strong and survive at extreme temperature. It sees every candidate in three dimensions: how strong it is, how well it survives, and how much the evidence can be trusted.
High-speed flight, propulsion and power systems need metals that stay strong and resist oxidation in the same temperature window, and very few materials do both.
Discovium evaluates alloys that have never been made, scoring every candidate for strength, survival and the evidence behind it.
Discovery happens in a dimension you can't plot by hand.
Materials are usually judged on a flat chart of two properties. DISCOVIUM adds the axis that chart leaves out: how much each prediction can be trusted. Seen in three dimensions, the space opens up, and the rare candidates worth making stand apart from the ones that only look good on paper.
Hot strength
How well an alloy holds its load as it approaches its service temperature, computed from the physics of the material rather than looked up.
Survival
How well an alloy resists attack by oxygen at temperature, screened against the published experimental record.
Confidence
How much evidence stands behind each number. This is the dimension that separates a real lead from a lucky guess, and the one the engine tracks for every candidate it touches.
Every hot-running machine meets the same wall.
High-speed flight, advanced propulsion, and next-generation power systems all need structural metals that keep their strength and resist oxidation in the same temperature window. Very few materials do both, and the ones that might are hidden in a composition space larger than every alloy ever produced.
Strength and survival pull apart
Chemistries that hold their strength at white heat tend to oxidize, while chemistries that protect themselves tend to soften. The useful ground between them is narrow.
Trial and error cannot reach it
Testing multi-element alloys one furnace run at a time would take lifetimes. Finding that narrow ground takes a search that sees the whole space at once.
A discovery loop that runs itself.
No one babysits jobs or hand-picks the next experiment. The engine decides where to look, does the work, checks its own output, and learns from every result. Select a stage to see what it does.
Propose
Chooses the next compositions where new information is worth the most.
Compute
Runs quantum-mechanical simulations of alloys that have never been made.
Derive
Turns electronic structure into mechanical behavior across temperature.
Screen
Scores oxidation survival against the experimental record, then weighs both properties together behind hard physics checks.
Deliver
Packages ranked candidates for each partner, tailored to their manufacturing constraints.
Every number shows where it came from.
Materials AI is crowded with predictions that cannot show their evidence. DISCOVIUM tags every property with its evidence tier, and a candidate's score can never exceed the quality of the evidence behind it.
An estimate cannot be promoted to a result by accident or by optimism. When our own checks find a problem, the affected values are downgraded or removed and the rankings are rebuilt. That discipline is why partners can build on what we hand them.
Proven in operation, not on slides.
Months of autonomous operation
The engine has run for months, working through storage failures, compute outages, and its own scaling limits, and recovering each time without losing data.
Tailored to your process
Deliverables are built around each partner's manufacturing constraints, including the elements, processes, and facilities they can actually use.
Physics before data
Because properties come from first principles, the engine evaluates alloys that have never been made and that no database contains.
Independently cross-checked
A separate machine-learning pipeline and outside first-principles calculations test the engine against itself on a benchmark of more than a thousand compositions.
Kept current
An automated weekly review of the open literature keeps performance targets aligned with the state of the field.
Honest about failure
When an internal hypothesis fails a pre-agreed test, we document it and correct the screening rules. That record is what makes the positive results trustworthy.
What changes with DISCOVIUM.
| Aspect | Conventional search | DISCOVIUM |
|---|---|---|
| View | Two properties on a flat chart. | Strength, survival, and confidence in one space. |
| Objective | Optimize one property, usually strength, and find the fatal flaw in the lab. | Weigh strength and survival together from the first calculation. |
| Data | Machine learning limited to what has already been measured. | Generates its own physics data continuously. The corpus grows every night. |
| Effort | Researcher-months per alloy family, with manual job management. | Samples, runs, recovers, and scores without supervision. |
| Trust | A number is a number. Its provenance rarely travels with it. | Every value carries its evidence tier. Estimates never pass as results. |
| Output | A generic ranked list. | Candidate packages tailored to each partner's manufacturing constraints. |
Bring us the environment. We'll find the material.
Tell us the temperatures, the atmosphere, and what your process can make. DISCOVIUM searches the space and returns candidates built for your constraints, each with its evidence attached.
- Structural alloys
- Protective coatings
- Ceramics
- Energy and power materials
- Harsh-environment components
Figures on this page are illustrative.