Model developers building specialized AI, ML, and surrogate systems for material and molecular problems run into the same wall: the ground truth they need to train against is costly, slow, or simply does not exist at the quality their models require. Subatomic Computing produces specialized training and evaluation datasets where conventional generation paths are inadequate.
An engagement produces a defined set of computational outputs structured for ingestion by model training and evaluation pipelines. The format is engineered for use, not just inspection.
Full microstate JSON across the parameter space of interest, structured for direct ingestion by training pipelines. Audit hashes on every record. Sample counts and parameter coverage scoped to the model class and objective.
Evaluation datasets generated under the same methodology but on parameter regions held out from training. Sized and structured for the evaluation metrics your team uses to qualify a model for production.
Documentation of what the dataset does and does not cover, with provenance records back to the engine runs that produced each record. The kind of audit trail required where a model's training data has to be defensible.
Model development for material and molecular problems is gated by training data. Experimental ground truth is slow and expensive. Public datasets are limited in coverage and shaped by what was historically interesting to publish. Synthetic data from classical simulators is constrained by the methods that generated it, and the structure the model learns reflects the assumptions of the simulator.
Subatomic Computing generates training data where the structure emerges from the Hamiltonian rather than from the data-generation pipeline's assumptions. The microstates that matter come from the engine; the regimes of the parameter space are surfaced rather than sampled; the trajectories carry path information that static datasets cannot.
What comes back is a dataset structured for direct use in training, with provenance and coverage maps that hold up to defensibility review. The kind of ground truth that conventional generation paths cannot produce at the quality and coverage your model needs.
The selective posture is easier to honor when the misconceptions are addressed up front. These are the things we are sometimes asked for and do not provide.
We do not tell you which model to train, what your architecture should be, or how to revise your training stack. We deliver datasets. Your team builds and evaluates the model.
We do not sell models, weights, or fine-tuned systems. The engagement is for the training and evaluation data, not for the model trained on it. Your model remains entirely yours.
We are not asking to co-author papers, share IP, share IP. This is delivery of proprietary computational outputs under defined terms. Production and delivery of all client datasets are air-gapped from end to end.
We engage selectively. The steps below describe what a serious inquiry moves through. Public detail is intentionally limited; method-level discussion follows fit review.
