The article examines how scientific literature, often used as a data source for AI in materials science, can contain hidden inaccuracies such as text-figure mismatches, ambiguous axis labels, unit inconsistencies, and missing measurement context. By tracing solid electrolyte conductivity values from original papers to curated datasets, the authors uncover recurrent errors that are numerically plausible yet hard to detect, leading to significant label noise in AI models. A cross-database example demonstrates that ambiguous reporting can cause a 100‑fold error in conductivity values, underscoring the need for traceable reporting, rigorous curation, and validation practices in AI-driven discovery.
arXiv:2607. 09806v1 Announce Type: cross Abstract: Artificial intelligence (AI) is transforming scientific discovery, but its effectiveness is fundamentally limited by the availability of structured scientific knowledge.
arXiv:2609.39340v1 Announce Type: new Abstract: Electrolyte formulation design requires balancing multiple physicochemical properties, yet existing models often focus on a limited subset. Learning ea...
arXiv:2606. 31366v1 Announce Type: cross Abstract: Driven by high-throughput experimentation, computational modeling, and artificial intelligence (AI), materials data has expanded at an unprecedented rate.
REPAIR is a self‑evolving data augmentation framework designed to improve scientific dense retrievers by addressing long‑tail concept gaps and fact sensitivity. It iteratively generates training data through diagnosis of long‑tail concepts, API‑guided evidence expansion, and hard negative mining, thereby grounding retrieval in factual reality. Experiments show that REPAIR outperforms 19 strong baselines across nine materials science and biomedical benchmarks.
arXiv:2507.09001v4 Announce Type: replace-cross Abstract: Machine learning (ML) models for electronic structure typically rely on large datasets generated by computationally expensive Kohn-Sham densi...
arXiv:2604. 22938v2 Announce Type: replace-cross Abstract: The promise of data-driven materials discovery remains constrained by the scarcity of large, high-quality, and accessible experimental datasets.
Precise retrieval of scientific information is fundamentally constrained by long-tailed concepts and high fact-sensitivity of scientific corpora. These challenges often limit the effectiveness of dens...
arXiv:2607. 21660v1 Announce Type: cross Abstract: Artificial intelligence (AI) is accelerating materials prediction and design by enabling efficient exploration of chemical and structural spaces, with particular promise for novel materials discovery.
arXiv:2508. 05762v2 Announce Type: replace-cross Abstract: Universal machine learning force fields (UMLFFs) promise to revolutionize materials science by enabling rapid atomistic simulations across the periodic table.
arXiv:2606. 07712v1 Announce Type: cross Abstract: Progress in AI-driven crystal materials science has so far been carried by narrow architectures purpose-built for individual tasks -- graph neural networks for property prediction, diffusion and flow-matching models for crystal generation -- each excelling within its niche yet unable to act as a shared backbone across the full spectrum of materials problems.
Scientific knowledge graphs organize entities and relations extracted from scientific literature, but they remain inherently incomplete. Missing typed links in such graphs can therefore represent plau...
HyGRAIL is a framework for discovering scientific hypotheses in incomplete knowledge graphs by combining a graph neural network (GNN) triage with large language model (LLM) review. The GNN scores candidate hypotheses and routes only ambiguous cases to the LLM, which receives structured evidence from the graph converted into natural language. Experiments on MatKG show HyGRAIL achieves the highest F1 score, improves over baselines, and cuts LLM calls by over 54%.