Research library
Materials science
View the four-date completion graphs and reconstruction notes.
Promise and evidence
AI could help discover and manufacture materials for batteries, electronics, catalysts, and stronger or lighter structures. The A-Lab study describes autonomous inorganic-material synthesis. A generated formula or predicted stable structure is a candidate, not automatically a new experimentally established material.
Apply the shared measurement protocol. Targets are proposed.
Dated baseline coverage
All linked measurable-outcome files below contain an evidence baseline reviewed November 7, 2026, with source dates, findings, limitations and follow-up measurements. These distinguish measured results from proxies and unknown values; they do not assert that proposed milestones are achieved. See the baseline coverage notes.
Progress estimates
Judgment-based estimates as of November 7, 2026, using the same 0–100% scale and five-part rubric as mathematics. 0% anchors the period before ChatGPT's public release in 2022; 100% requires AI to manage this field's continuing advancement without human participation. Confidence is low. These are evidence-based judgments, not empirical completion measurements, forecasts or percentages of jobs replaced.
| Measurable outcome | Estimated progress | Execution / breadth / agenda / validation / improvement (each out of 20) |
|---|---|---|
| Deployment value | 15% | 5 / 5 / 0 / 5 / 0 |
| Discovery efficiency | 25% | 5 / 5 / 5 / 5 / 5 |
| Functional improvement | 40% | 15 / 5 / 5 / 10 / 5 |
| Identity and novelty | 15% | 5 / 5 / 0 / 5 / 0 |
| Scale and durability | 30% | 10 / 5 / 5 / 5 / 5 |
| Synthesis success | 30% | 10 / 5 / 5 / 5 / 5 |
Each linked outcome explains its score, cites its dated evidence baseline and identifies the next measurement. Zero indicates no qualifying post-anchor progress established by the reviewed evidence for that outcome, not proof of no relevant capability. Do not average these overlapping indicators into a field-wide percentage or interpret their differences as a precise ranking of fields. All outcomes reaching 100% would still require a separate field-wide demonstration of sustained autonomous advancement.
Measurable features
| Feature | Measurement and acceptance rule |
|---|---|
| Synthesis success | Independently confirmed target materials / all selected candidates attempted, with unsuccessful recipes retained. |
| Identity and novelty | Count materials with measured composition and structure, prior-art search, and independent review distinguishing new phases from known materials or routine substitutions. |
| Functional improvement | Application-specific measurements such as battery Wh/kg and cycle life, conductivity at specified temperature, or strength at specified density. Fix test conditions before selection. |
| Discovery efficiency | Days, human hours, experiments, and total dollars from target specification to independently reproduced sample meeting all property requirements. |
| Scale and durability | Yield, batch variability, degradation, and property retention when production scale increases. |
| Deployment value | Cost per qualified unit and lifetime performance against the existing material in a representative device. |
Evidence requirements
Provide raw characterization data, uncertainty, synthesis records, impurity analysis, and application tests. Require an independent laboratory to reproduce synthesis and functional performance. Count novelty and usefulness separately: a new phase can have no practical advantage, and a useful processing improvement can involve a known material.
Proposed milestones
Independently reproduce at least five target materials from a preregistered campaign of 20 candidates; report the full campaign cost and every failure.
Demonstrate at least 10% improvement in a prespecified useful property over the strongest matched baseline while satisfying all other required properties.
Retain the gain in three independently manufactured batches at ten times the initial synthesis scale.
Validate a material in a working device and measure delivered lifetime cost. For AI feedback, separately test whether a material used in computing or energy infrastructure improves subsequent AI development rather than merely assuming that connection.