Decision-first AI for battery materials discovery.
ASTR-1 accelerates battery materials discovery by helping researchers decide which candidates to advance, repair, reject, validate, or prepare for synthesis.
From generating candidates to deciding on them.
Generative models can produce millions of plausible battery materials. The harder question — the one that actually determines whether research time is well spent — is which of those candidates deserve a researcher's next hour, next experiment, next gram of precursor.
ASTR-1 is built around that question. It treats discovery as a sequence of governed decisions, each backed by the right evidence at the right cost.
A deep-tech company at the intersection of AI, materials science, and quantum-inspired computing.
Neognos Tech brings structure and clarity to battery materials discovery. We build the architecture that turns vast candidate spaces into a series of well-reasoned decisions — advance, repair, reject, validate, or move to synthesis — so research effort goes toward the candidates that actually warrant it.
Battery materials discovery is a decision problem in disguise.
Modern AI can generate candidate materials almost without limit. The bottleneck has moved upstream — to deciding which of those candidates are worth a researcher's time.
Vast candidate space
Battery materials research spans an enormous combinatorial space of compositions, structures, dopants, and processing routes.
Evaluation is costly
Each candidate consumes compute, reagents, and researcher attention. Naive parallelism is not a strategy.
Many failure modes
Candidates fail for instability, poor performance, toxicity, scarce elements, cost, or sheer synthesis difficulty.
Deciding is the bottleneck
The hard problem was never generating candidates — it's deciding which ones justify further effort.
One architecture, tuned for energy storage.
ASTR-1 is not a general-purpose materials generator. It is shaped around the specific failure modes, validation costs, and synthesis constraints that govern modern battery chemistries.
See the architecture- Battery-first scope — cathodes, anodes, electrolytes, interfaces.
- Decision-first orchestration via the Master Route Controller.
- Uncertainty-aware gating between every stage.
- Failure-learning flywheel built into the pipeline.
Built for decisions, not just candidates.
ASTR-1 differs from generic generative pipelines in how it governs the journey from candidate to commitment.
Decision-first architecture
ASTR-1 is organised around the decision a researcher needs to make — not around a single model or a single dataset.
Governed AI workflows
A Master Routing Controller orchestrates the seven internal pipelines and decides when to escalate, repair, or reject.
Evidence-based progression
Candidates move forward only when the evidence supports it. Low-confidence outputs are escalated, not silently accepted.
Traceable scientific decisions
Each candidate carries a record of which pipelines ran, what they returned, and which gate let it advance.
Human-guided validation
Researchers stay in the loop. Physical testing is permission-gated and the workflow surfaces context, not just answers.
Failure-learning flywheel
Negative results are first-class. Every failure feeds back into routing so the next decision is better informed.
An interactive view of the ASTR-1 decision pipeline.
From an initial research request to a reported decision — each node produces evidence the controller uses to decide what happens next. Hover or tap a stage.
A research goal enters the system — target chemistry, application envelope, and constraints define the run.
Five intersecting disciplines.
ASTR-1 is the artefact, but the research direction is broader.
- 01
AI
Learned models for property prediction, routing, and repair across the candidate journey.
- 02
Battery Materials
Cathodes, anodes, electrolytes, and interfaces — the chemistries that define modern energy storage.
- 03
Quantum-Inspired Computing
QIGA explores candidate spaces using quantum-inspired formulations, implemented classically.
- 04
Materials Informatics
Structured representations and failure databases that turn negative results into reusable knowledge.
- 05
Sustainable Energy
Prioritising earth-abundant, recyclable, and lower-impact chemistries in scoring and feasibility gates.
The stack behind ASTR-1.
Eight building blocks composed into a single decision-first architecture.
Language interface
A GPT-powered interface that turns researcher intent into structured discovery runs, explanations, and reports.
Master Routing Controller
The control layer that decides which ASTR-1 pipeline, gate, or repair loop runs next.
Quantum Inspired Generation
Quantum-inspired generation of battery-material candidates across constrained composition and structure spaces.
Candidate Validation
Candidate validation through internal screening, surrogate checks, and escalation to higher-fidelity methods when needed.
Dopant and composition repair
Targeted modifications to fix specific weaknesses rather than discarding promising candidates.
Hybrid property prediction
Battery-relevant properties estimated through learned models, physics-informed logic, and multi-fidelity prediction.
Synthesis and failure learning
Practical synthesis checks and failed-run memory turn both valid and rejected candidates into reusable intelligence.
Uncertainty-aware routing
Confidence and risk signals decide whether a candidate is accepted, escalated, repaired, or rejected.