arXiv:2608. 15900v1 Announce Type: cross Abstract: Data-driven materials discovery interpolates more reliably than it extrapolates and seldom reaches new structure types.
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.
Crystal generators and tool-using agents propose structures faster than density functional theory (DFT) energy and phonon calculations or experiments can assess them. Deciding which candidates merit e...
The paper reports that autonomous agents can generate, test, and refute millions of candidate structure-plausibility laws, ultimately producing eight Plausibility Rules for Inorganic Structures (PRIS) that capture five key mechanisms of crystal stability. PRIS achieves high agreement with experimental structures (82–99%) and outperforms traditional Pauling rules, detecting damaged crystals with 87.9% accuracy and correlating strongly with synthesizability. By integrating PRIS with a synthesis score (PSS), the authors demonstrate significant reductions in expensive DFT validation and improved inverse-design efficiency, while also providing chemical explanations for failures and anomalies in crystal data.
arXiv:2609.01076v1 Announce Type: cross Abstract: Crystal generators can now propose periodic structures, but their control interfaces remain poorly matched to the mixed descriptors used in materials...
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.
The article introduces the concept of agentic programs—scientific software that blends deterministic algorithms with bounded large‑language‑model (LLM) judgment, task‑specific verification, episodic maturation, and full delegation in production. It argues that recent LLM‑based agents enable this new form of computational materials science software. The authors illustrate the idea with DeMARS, an agentic program designed to build atomistic models from experimentally measured disordered crystal structures.
The thesis presents AI frameworks that accelerate crystalline materials discovery by tackling both crystal property prediction and crystal structure generation. It introduces CrysXPP, CrysGNN, and CrysMMNet for efficient, data‑sparse property prediction using graph autoencoding, self‑supervised pretraining, and multimodal learning. For generation, TGDMat is a text‑guided diffusion model that jointly learns lattice parameters, atomic types, and coordinates, enabling valid, stable, and conditionally generated periodic materials.
Crystal generators can now propose periodic structures, but their control interfaces remain poorly matched to the mixed descriptors used in materials design. Text provides a compact way to combine com...
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.
This thesis develops robust and efficient AI frameworks for accelerating crystalline materials discovery by addressing both major stages of the materials-design pipeline: crystal property prediction a...
The paper introduces SPARC, a reinforcement learning framework that generates crystalline materials while respecting symmetry constraints essential for meaningful physical properties. SPARC is applied to two tasks: optimizing uniaxial dielectric anisotropy, which requires specific crystal classes, and maximizing spectroscopic limited maximum efficiency, a scalar objective that lets the algorithm discover suitable crystallographic motifs. The results demonstrate that symmetry is a foundational requirement for producing robust, realizable functional materials.
arXiv:2506. 05616v3 Announce Type: replace Abstract: We aim at designing language agents with greater autonomy for crystal materials discovery.