Complex chemical systems containing heterogeneous substructures are common in real-world applications. Various physical phenomena of the complex chemical systems are derived from the interactions betw...
arXiv:2607. 24818v1 Announce Type: cross Abstract: Accurate prediction of crystal properties remains a key challenge in computational materials science.
By Sanjay Chakraborty
arXiv:2607. 29510v1 Announce Type: cross Abstract: High-entropy perovskite oxides (HEPOs) represent a chemically complex class of materials with promising functional properties, yet their vast compositional space and, chemical/structural disorder pose significant challenge for accurate property prediction.
By Panupol Untarabut, Narjes Jomaa, Sylvian Cadars, Olivier Masson, Samuel Bernard, Assil Bouzid, Santanu Saha
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:2608. 14640v1 Announce Type: cross Abstract: Stacked bilayer materials exhibit rich stacking-dependent properties driven by the interplay between strong intra-layer bonding and weak inter-layer van der Waals interactions.
By An Vuong, Chen Zhao, Jin Hu, Shui-Qing Yu, Xintao Wu
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...