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.
By Kishalay Das
arXiv:2606. 00776v1 Announce Type: new Abstract: Fast and accurate prediction of crystal properties is a central challenge in new materials design.
By Shrimon Mukherjee, Kishalay Das, Partha Basuchowdhuri, Pawan Goyal, Niloy Ganguly
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.
By Zhan'ao Yao, Boxuan Zhang, Jingyuan Shu, Xiaoyu Wu, Rongyan Wang, Linjing Li, Dajun Zeng, Yudong Yao, Tingwei Chen, Youwei Wang, Xiaolin Zhao, Jiahui Shi, Jianjun Liu
arXiv:2607. 08996v1 Announce Type: cross Abstract: Graph Neural Networks have emerged as a powerful tool for the fast and accurate prediction of various crystal properties.
By Shrimon Mukherjee, Kishalay Das, Partha Basuchowdhuri, Pawan Goyal, Niloy Ganguly
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.
By Chenmu Zhang, Boris I. Yakobson
arXiv:2604. 13354v2 Announce Type: replace-cross Abstract: The discovery of inorganic crystal structures with targeted properties is a significant challenge in materials science.
By Auguste de Lambilly, Vladimir Baturin, David Portehault, Guillaume Lambard, Nataliya Sokolovska, Florence d'Alch\'e-Buc, Jean-Claude Crivello
arXiv:2607. 24818v1 Announce Type: cross Abstract: Accurate prediction of crystal properties remains a key challenge in computational materials science.
By Sanjay Chakraborty
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.
By Anand Babu, Rog\'erio Almeida Gouv\^ea, Gian-Marco Rignanese
arXiv:2605. 17254v3 Announce Type: replace Abstract: Property prediction and inverse structural design of catalytic materials are typically modeled as two independent tasks: the former predicts target properties from given structures, whereas the latter generates candidate structures according to desired properties.
By Yanjie Li, Jian Xu, Xu-Yao Zhang, Shiming Xiang, Nian Ran, Weijun Li, Cheng-Lin Liu
arXiv:2609.00059v1 Announce Type: cross
Abstract: Materials property prediction remains difficult in low-data settings, where many target properties are supported by only a limited number of labeled...
By Weiran Wang, Xintong Huo, Yueying Wang, Yusi Fan, Wenyan Wang, Xin Feng, Ruihao Xin, Lan Huang, Kewei Li, Fengfeng Zhou
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.
By Xianyuan Liu, Charles Anjah, Benjamin E. Jolly, Jonathon F. S. Markanday, Joshua Berry, Haolin Wang, Nicola A. Morley, Robert D. J. Oliver, Alexandra J. Ramadan, Delvin Ce Zhang, Katerina A. Christofidou, Haiping Lu
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.
By Germain Poloudenny, Ya\"el Fr\'egier, Arnaud Demorti\`ere