Sciences
Post-training for scientific discovery
Fine-tune foundation models on your domain data — proteins, molecules, climate, genomics. Mixtrain handles the data infrastructure, experiment tracking, and evaluation so you can focus on the science.
Your expertise is the science, not the infrastructure
Research teams are spending more time building training pipelines, managing dataset versions, and debugging distributed training than doing actual research. When a reviewer asks to reproduce an experiment from six months ago, it takes days to reconstruct the environment.
Mixtrain gives scientific teams production-grade training infrastructure with the reproducibility guarantees research demands. Every dataset version, config, seed, and result is captured automatically. Re-run any experiment exactly, or share it with a collaborator in one click.
Works across scientific domains
Mixtrain works with the data formats, evaluation methods, and collaboration patterns researchers already use.
Structural Biology
Fine-tune protein and molecule models with domain-specific tokenization. Evaluate on binding affinity, RMSD, structural accuracy, and functional predictions. Import directly from PDB.
Climate & Earth Science
Work with large-scale geospatial and temporal datasets. Build downscaling models, weather predictors, and climate emulators. Evaluate with domain skill scores, not just loss curves.
Genomics
Build pipelines for sequence analysis, variant effect prediction, and phenotype modeling. Version reference genomes alongside training data. Evaluate with AUROC, calibration, and clinical relevance metrics.
What you get
Full reproducibility
Every dataset version, training config, random seed, and evaluation result is captured. Generate reproducibility artifacts for publication with one command.
Domain-specific evaluation
Define metrics that matter for your field — RMSD, skill scores, AUROC, calibration curves. Compare models on science, not generic loss.
Large-scale data management
Version datasets with columnar storage. Import from lab instruments, public repositories (PDB, GEO, ERA5), or cloud storage.
Collaborative workspaces
Share experiments across lab members and collaborators with fine-grained access control. Build on shared datasets and compare approaches.
Flexible compute
Run training on your own HPC cluster, cloud GPUs, or Mixtrain-managed compute. Same workflow regardless of where the jobs run.
Experiment comparison
Compare runs across any metric. Visualize predictions, track hyperparameter sweeps, and identify the best models across hundreds of experiments.
Custom training scripts
Bring your own PyTorch, JAX, or framework-specific training code. Mixtrain wraps your scripts with tracking and infrastructure — no rewrites needed.
Publication-ready exports
Export models, datasets, configs, and evaluation results in formats ready for supplementary materials or public release.
Accelerate your research
Spend less time on infrastructure, more time on science. Free for individual researchers.