The paper introduces a new way to evaluate materials graph neural networks (GNNs) by measuring how many trainable parameter‑space directions are needed to achieve good performance. Using random‑subspace intrinsic‑dimension analysis, the authors train CGCNN, ALIGNN, and DimeNet++ on six prediction tasks and plot recovery curves that separate final accuracy from the dimensional demand required to reach it. The study finds that different tasks and architectures vary in how sensitive they are to dimensional restriction, revealing insights that final error metrics alone miss.
By Shehroz Ahmad Shoaib, Kangming Li
arXiv:2607. 24818v1 Announce Type: cross Abstract: Accurate prediction of crystal properties remains a key challenge in computational materials science.
By Sanjay Chakraborty
The study shows that electronic structure datasets used for machine learning contain significant redundancy due to low intrinsic dimensionality. Random pruning and a coverage-based strategy can reduce dataset size by up to two orders of magnitude while preserving chemical accuracy and generalizability, cutting training time by a factor of three or more. The essential information lies on a low‑dimensional, non‑linear manifold, suggesting that large datasets may be largely overlapping and that minimal, representative datasets could suffice for accurate predictions.
By Sazzad Hossain, Ponkrshnan Thiagarajan, Shashank Pathrudkar, Stephanie Taylor, Abhijeet S. Gangan, Amartya S. Banerjee, Susanta Ghosh
arXiv:2512.22740v4 Announce Type: replace
Abstract: Multi-task learning (MTL) in materials science relies on the assumption that physically related properties share learnable representations. We chal...
By Sungwoo Kang
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:2608.23874v1 Announce Type: cross
Abstract: Predicting thermal stability during handling and storage is essential for the design of safe and reliable energetic materials. However, experimental...
By Megan C. Davis, R. Seaton Ullberg, Jeremy N. Schroeder, Andrew H. Salij, Marc J. Cawkwell, Christopher J. Snyder, Ivana Matanovic, Wilton J. M. Kort-Kamp
A robust machine‑learning framework is presented for predicting the thermoelectric figure of merit (zT) in half‑Heusler compounds, focusing on unseen compositions. The method uses a PCA‑based train/test split, Bayesian hyperparameter tuning, and feature selection to improve Random Forest, XGBoost, and Neural Network models, while SISSO provides interpretability. Validation includes reproducing known optimal zT values, predicting new compounds (CeCoBi, CeIrBi), and high‑throughput screening of 2.46 million substitution combinations to identify promising candidates.
By Shoeb Athar, Adrien Mecibah, Philippe Jund
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:2509.05909v3 Announce Type: replace-cross
Abstract: The reliable identification of magnetic ground states remains a major challenge in high-throughput materials databases, where density functio...
By Ahmed E. Fahmy
The paper introduces UniTB, a universal, E(3)-equivariant Hamiltonian framework that blends density‑functional tight‑binding priors with deep learning to predict electronic structures efficiently. UniTB corrects a prior Hamiltonian via a mixture‑of‑experts network and a charge‑equilibration head, ensuring physical downstream properties and suppressing spurious states. Trained on over 29,000 crystals, UniTB achieves sub‑meV accuracy, matches DFT predictions for amorphous Ge‑Sb‑Te, and enables rapid screening of more than 16,000 materials for industrial applications.
By Mingkang Liu, Zhanghao Zhouyin, Guanghui Wang, Daye Zheng, Lei Shen, Qiangqiang Gu
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.
By Zhenhao Zhou, Salman Bin Kashif, Jin-Hu Dou, Chris Wolverton, Kaihang Shi, Tao Deng, Zhenpeng Yao
arXiv:2610.07712v1 Announce Type: cross
Abstract: Existing molecular and materials learning approaches often rely on a limited set of structural representations, which may capture only selected aspec...
By Yiming Ren, Xiang Liu, Mustafa Hajij, Pietro Li\`o, Guo-Wei Wei