Molecular simulation
DFT-grade energies, geometries and force curves for small and mid-size molecules. The training substrate for reactive AI.
QMATRIX pairs domain-expert review with cryptographic provenance for molecular, materials and optimization datasets. Trainers get verified scientific data. Experts get paid in $MATRIX.
Generic labeling networks cannot verify a DFT calculation. QMATRIX routes every submission through compute and credentialed experts before it reaches a trainer.
Labs and research groups post datasets under a scientific class. Every submission carries provenance, method, and reproducibility metadata.
DFT and reference workflows re-run on standardized environments. Results are hashed on-chain alongside the inputs that produced them.
Verified domain experts stake $MATRIX to attest each record. Consensus across attesters, not a single reviewer, produces the signal.
Trainers pull verified sets by class, method, and confidence. Attesters and contributors earn on downstream use.
DFT-grade energies, geometries and force curves for small and mid-size molecules. The training substrate for reactive AI.
Band gaps, formation energies, elastic and dielectric tensors across crystalline and amorphous systems.
Transition states, activation energies and reaction networks that closed catalysis models depend on.
Affinity, pose and free-energy windows attested by structural biology reviewers, not crowd labelers.
QUBO, Ising and combinatorial benches with verified optimal or best-known bounds for benchmarking.
Coupled-cluster and multireference reference sets that pin the ceiling for approximate methods.
Every cell is one scientific dataset moving through submission, DFT computation, expert attestation and final verification. Click a row, column or cell to inspect the pipeline.
Quantum-AI is starving for the one data class nobody can crowdsource.
Off-chain black boxes. No token, no portable reputation, no neutral attestation surface.
Target broad, general-purpose data. Not built for DFT-grade scientific labels.
Runs the simulations. Complements QMATRIX. Does not attest who labeled what, or how well.
Pairs domain-expert attestation with the scientific data class. The on-chain Mercor or Surge for quantum-AI data.
$MATRIX is the coordination asset. Attesters stake to review, trainers pay to route, contributors earn on downstream use. One token, one grid, one clearing layer for scientific training data.
Attesters bond $MATRIX to review a class.
Trainers pay to pull verified data.
Contributors accrue on downstream use.
Consensus drift is penalized economically.
Credentialing DFT, materials and structural-biology reviewers.
Catalysis, protein ligand and optimization pipelines go live.
Verified sets routed to trainers by class, method and confidence.
QMATRIX becomes the clearing layer for scientific training data.
$MATRIX is live on Virtuals Protocol on Robinhood Chain. Join the attester network or follow the launch. QMATRIX is the on-chain Mercor for scientific training data.