An AI that has read the materials literature
Ask the questions you would ask a senior materials engineer — and get an answer that shows where each number came from, or admits it is running on general knowledge.
How an answer is assembled
- 01 Your question Read for compounds, phases and conditions
- 02 Tools run Structure search, MP properties, literature
- 03 Answer written Against the evidence just retrieved
- 04 Sources listed Every database and DOI actually used
Why a general chatbot is not enough
General assistants are fluent about materials and quietly unreliable about numbers — a yield strength to three significant figures with no condition, no standard, no source. That is worse than no answer, because it looks like data.
Here, anything not backed by a tool call is labelled as materials-science knowledge to be verified against standards or datasheets.
What people ask it
| Physical metallurgy | Phase transformations, heat treatment windows, precipitation sequences, why your hardness came out low. |
|---|---|
| Joining | Welding dissimilar pairs, filler selection, preheat and PWHT, brazing, adhesives, galvanic risk at the joint. |
| Degradation | Corrosion mechanisms, stress-corrosion cracking, UV and hydrolytic ageing of polymers, high-temperature oxidation. |
| Failure analysis | Reading a fracture surface, fatigue versus overload, creep, embrittlement mechanisms and what to test next. |
| Characterisation | Which technique answers your question — XRD, SEM/EDS, DSC, tensile, hardness — and how to read the output. |
| Processing | Castability, formability, machinability, additive manufacturing parameters and the defects each route brings. |
Where to go next
Chat is for understanding. When you need a decision, the material selector turns requirements into a ranked shortlist, and the knowledge base gives you the reference data to check both against.
- Materials Project — computed properties
- OQMD — formation energy, hull, band gap
- Open Materials Database — structures
- OpenAlex — scholarly literature
- arXiv — preprints
- PubChem — compound data
Try the same databases yourself in the structure search.
20 AI requests a month, no card. Enough to test it against a question you already know the answer to — which is the only sensible way to evaluate one of these.
Questions about the chat
How is this different from a general-purpose chatbot?
Three things: the system prompt is written for materials engineering, the model can call live materials databases and literature APIs while it answers, and it is required to attribute every figure — naming the database or DOI, or flagging the statement as general knowledge that you need to verify.
Which databases can it reach?
Crystal structures and DFT properties from the Materials Project, OQMD and the Open Materials Database over OPTIMADE; literature from OpenAlex and arXiv; compound data from PubChem. When a mirror is down it retries another and tells you which one answered.
Does it stream answers?
Yes — answers appear as they are generated, so a long metallurgical explanation is readable before it finishes.
What happens when it does not know?
It is instructed to say so and to mark the gap, rather than filling it with a plausible number. If a tool call fails or returns nothing, that is stated in the answer instead of being quietly ignored.
Put an engineer's AI on the problem
Free plan, no card required. 20 AI requests a month, and the whole knowledge base stays open to everyone.