arXiv:2607. 24785v1 Announce Type: cross Abstract: Efficient exploration of the photonic crystal (PhC) lattice design space is essential for developing photonic crystal surface-emitting lasers.
arXiv:2509. 15908v3 Announce Type: replace-cross Abstract: Nanoporous materials hold promise for diverse sustainable applications, yet their vast chemical space poses challenges for efficient design.
arXiv:2606. 00776v1 Announce Type: new Abstract: Fast and accurate prediction of crystal properties is a central challenge in new materials design.
arXiv:2502. 02748v4 Announce Type: replace Abstract: Predicting properties of crystals from their structures is a fundamental yet challenging task in materials science.
arXiv:2607. 24818v1 Announce Type: cross Abstract: Accurate prediction of crystal properties remains a key challenge in computational materials science.
arXiv:2608. 06448v1 Announce Type: cross Abstract: Recovering a periodic 3D crystal structure from sparse, unindexed electron diffraction (ED) observations is a challenging generative inverse problem.
arXiv:2608. 13826v1 Announce Type: cross Abstract: Machine learning is increasingly used to learn structure property relationships from spectroscopic and diffraction data, yet its adoption in materials discovery is often limited by poor interpretability of model predictions.
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.
arXiv:2607. 12380v1 Announce Type: new Abstract: Small molecules, crystals, and proteins all reduce to atoms in 3D space, yet their generative pipelines remain fragmented across domains, each with its Small molecules, crystals, and proteins all reduce to atoms in 3D space, yet their generative pipelines remain fragmented across domains, each with its own graph, equivariant, or frame-based architecture.
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...
arXiv:2606. 14003v1 Announce Type: cross Abstract: Determining the crystal structure of a material from its powder X-ray diffraction (PXRD) pattern is a central challenge in materials science.
arXiv:2606. 00401v1 Announce Type: cross Abstract: Simulating large molecular systems comprising thousands of atoms requires highly scalable methodologies.
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.