NexusLabs deploys autonomous discovery loops that hypothesise, simulate, validate, and learn — continuously, across scientific domains. One architecture. Many frontiers. The first product, NexCon-03, runs this loop against wide-bandgap semiconductor materials.
Average time from scientific hypothesis to validated discovery in materials, drug design, quantum systems, and catalysis.
Typical expenditure for a full experimental campaign across synthesis, characterisation, and iteration in any deep-science domain.
Configuration-space candidates in any domain that no human research team will ever screen manually in their lifetime.
Scientific discovery is bottlenecked by the same constraint everywhere — human bandwidth. A researcher can hold one hypothesis at a time, run one experiment a month, and iterate on a timescale of years. NexusLabs replaces that bottleneck with an autonomous system that never stops — and that assists researchers, fabs, and labs across every domain it touches.
We pair the reasoning power of Large Language Models with the physical accuracy of Large Quantitative Models. A domain-agnostic autonomous loop that hypothesises, simulates, validates, and learns — without human intervention. Deploy the same architecture against materials, drug discovery, quantum chemistry, or any domain with a quantitative simulation layer.
Rather than searching known solution spaces, the LLM orchestrator generates novel, falsifiable scientific hypotheses grounded in domain knowledge — then translates each into a formal Experiment Specification the quantitative engine understands exactly.
Every LQM prediction carries a calibrated confidence score. Structures where the model is uncertain are automatically escalated to an exact domain oracle — ensuring discoveries are physically grounded, not pattern-matched.
Each oracle call labels a new data point. The LQM micro-fine-tunes in real time. Within a single discovery session, the model's accuracy in the target chemical region improves measurably — without pre-curated training data.
You specify the scientific domain, the property envelope you're targeting, and the performance threshold to beat. The engine handles everything else — no workflow configuration, no manual pipeline setup required.
The LLM orchestrator generates falsifiable scientific hypotheses grounded in domain-specific knowledge — crystal chemistry, pharmacology, quantum mechanics, or any target field. Each is translated into a formal Experiment Specification the quantitative engine understands unambiguously.
Thousands of candidate configurations are generated and evaluated in parallel. Domain properties — formation energy, binding affinity, bandgap, reaction yield — are predicted by the LQM stack in milliseconds. Each evaluation replaces hours of conventional first-principles computation.
Candidates where the LQM reports high epistemic uncertainty are escalated to an exact domain oracle. Each oracle call labels a new training point. The LQM fine-tunes in the target subspace — improving accuracy where it matters most, with no pre-curated dataset.
Candidates that satisfy domain stability criteria, pass novelty scoring against known databases, and meet your specified property thresholds are surfaced. Ranked, confidence-annotated, and formatted for direct hand-off to experimental validation teams.
Each NexusLabs product deploys the LLM + LQM discovery loop in a specific scientific domain — trained, calibrated, and actively learning within that domain's physical constraints. One scalable architecture. Many frontiers.
Autonomous discovery of wide-bandgap semiconductor materials. NexCon-03 runs the full NexusLabs LLM + LQM loop against the crystallographic space of compound semiconductors — evaluating formation energy, thermodynamic stability, bandgap, and dopant defect energetics at scale. Designed for fabs, university research groups, national / military labs, and advanced hardware programmes worldwide.
The LLM hypothesises binding targets; the LQM evaluates binding affinity, ADMET, and synthesizability — autonomously and continuously.
Discovery of topological insulators, superconductors, and quantum spin liquids — guided by band topology, phonon stability, and correlated electron physics.
Novel catalysts for hydrogen evolution, CO₂ reduction, and nitrogen fixation — adsorption energies, reaction barriers, catalyst stability.
NexCon-03 focuses on compound families where the gap between available materials and required performance is largest — and where autonomous simulation can compress years of experimental iteration into a single discovery session.
Aluminium-gallium nitride alloy with tunable bandgap from 3.4 → 6.2 eV. Target: deep-UV LEDs, sterilisation sources, space-grade UV sensors.
Deep-UV LEDs, sterilisation sources, biosensing, and space-grade UV sensors. Bandgap tunable from 3.4 → 6.2 eV by Al composition, enabling precision spectral engineering across the UV range.
Optimising n-type and p-type doping for HEMT power devices, 5G RF front-ends, and defence radar. Mapping donor formation energies across the convex hull to identify low-resistance ohmic contacts at scale.
High-power MOSFETs for EV drivetrains, grid infrastructure, and rail traction. Targeting thermal conductivity and breakdown-field improvements over 4H-SiC for wide-bandgap power conversion.
HfO₂ phase engineering for next-generation gate stacks on GaN and SiC. Targeting metastable phases with higher dielectric constant than Al₂O₃ — a shared bottleneck across every advanced transistor node.
Evaluation at scale: each material family is screened across millions of candidate structures per session, cross-referenced against Materials Project, ICSD, and AFLOW to score novelty, synthesizability, and patentability. Applicable to any fab, lab, or R&D programme worldwide.
From BITS Pilani — building the architecture, the loop, and the products.
Computational physics, LQM architecture, materials science, and semiconductor physics. Deep focus on quantum and solid-state physics as the theoretical foundation for hyperautomated scientific discovery.
Machine learning, deep learning, and AI infrastructure. Specialises in agentic AI architecture — building the autonomous reasoning and orchestration layer that drives the LLM-LQM discovery loop.
Fab, pharma, university research group, national lab, deep-tech, or investor — we'd like to have a technical conversation about what you're building.