arXiv:2608. 15900v1 Announce Type: cross Abstract: Data-driven materials discovery interpolates more reliably than it extrapolates and seldom reaches new structure types.
Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.
arXiv:2606. 29717v1 Announce Type: cross Abstract: Predicting a material's properties from its structure is a central, fast-advancing problem in computational materials science.
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.
arXiv:2602. 17176v4 Announce Type: replace-cross Abstract: Crystal structure prediction (CSP), which aims to predict the 3D atomic arrangement of a crystal from its composition, is central to materials discovery and mechanistic understanding.
arXiv:2410. 08562v5 Announce Type: replace-cross Abstract: Advanced crystal design can accelerate materials discovery across applications from photovoltaics to spintronics.
arXiv:2604. 11827v2 Announce Type: replace-cross Abstract: Machine learning is revolutionizing chemistry.
arXiv:2606. 02507v1 Announce Type: cross Abstract: Inverse materials design is shifting materials discovery from forward prediction to targeted proposal of candidates that satisfy objectives under physical constraints.