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ChemReporter can streamline the curation of massive chemical datasets, enabling MLIP training on billion-structure datasets without memory constraints.
Open-source mlip v2 slashes the computational cost of atomistic simulations while maintaining near ab initio accuracy, thanks to a high-performance backend and scalable Mixture-of-Experts architecture.
Pretrained MLIPs already encode sufficient information in their latent spaces to guide active learning, enabling efficient fine-tuning without uncertainty quantification.