Software / AI
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Sources and links on page 482 (7)
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- https://www.nature.com/articles/s41586-023-06735-9?utm_source=chatgpt.com
- https://arxiv.org/html/2410.12771v1?utm_source=chatgpt.com
- https://opencatalystproject.org/?utm_source=chatgpt.com
- https://pubs.acs.org/doi/10.1021/jacsau.4c01160?utm_source=chatgpt.com
- https://huggingface.co/datasets/SandboxAQ/aqcat25-dataset
- https://www.anl.gov/article/building-ai-foundation-models-to-accelerate-the-discovery-of-new-battery-materials?utm_source=chatgpt.com
- https://research.ibm.com/blog/foundation-models-for-materials?utm_source=chatgpt.com
Text on page 482
Large Quantitative Models (LQMs) Pipelines - How to Train & Deploy LQM Models Effectively Pretrain chemistry foundation models on large open source materials datasets, then efficiently fine-tune with targeted, high-fidelity DFT data for battery-specific properties (voltage, stability, ionic transport, interfacial chemistry) to accelerate discovery-to-deployment.
END-TO-END PIPELINE Pretraining Datasets – Large, open multi-domain materials datasets (Materials Project, OC20/OC25, AQCat25)
Self-Supervised Foundation Pretraining – GNNs/ Transformers architectures (e.g. MACE, GNoME) learn universal embeddings
Next-gen Cathodes
Chemistry-specific Fine-Tuning – Targeted, high-fidelity DFT datasets and narrow chemistry classes (e.g. cathode, solidstate electrolytes)
New SSEs
Validation & Guardrails – DFT/MD benchmarks, uncertainty quantification, physics & chemistry sanity checks
Layered Structures
Source: GNoME (2023), OMat24 (2024), Open Catalyst Project, JACS Au (2024), AQCat25 (2025), Argonne/IBM (2025)
Deployment & optimization – API serving, CI/CD, GPU scaling, Bayesian optimization integration & closedloop workflows
Key Metrics
Time-to-candidate ↓ 90% vs. brute-force DFT
RMSE <0.1 eV for formation energies
5× higher validated material hit-rate
20× compute cost reduction
Extracted from the PDF; charts and images may read imperfectly.
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