Machine learning-driven elasticity prediction in advanced inorganic materials via convolutional neural networks
State Key Laboratory of Porous Metal Materials, School of Materials Science and Engineering, Xi’an Jiaotong University（西安交通大学材料科学与工程学院多孔金属材料国家重点实验室）；National Key Laboratory of Computational Physics, Institute of Applied Physics and Computational Mathematics（应用物理与计算数学研究所国家计算物理学重点实验室）；State Key Laboratory for Mechanical Behavior of Materials, School of Materials Science and Engineering, Xi’an Jiaotong University（西安交通大学材料科学与工程学院材料力学行为国家重点实验室）
Yujie Liu, Zhenyu Wang, Hang Lei, Guoyu Zhang, Jiawei Xian, Zhibin Gao, Jun Sun, Haifeng Song, Xiangdong Ding
Inorganic crystal materials have broad application potential due to excellent physical and chemical properties, with elastic properties (shear modulus, bulk modulus) crucial for predicting materials' electrical conductivity, thermal conductivity and mechanical properties. Traditional experimental measurement suffers from high cost and low efficiency, while theoretical simulation and graph neural network-based machine learning methods--especially crystal graph convolutional neural networks (CGCNNs)--have become effective alternatives, achieving remarkable results in predicting material elastic properties. This study trained two CGCNN models using shear modulus and bulk modulus data of 10987 materials from the Matbench v0.1 dataset, which exhibit high accuracy (mean absolute error <13, coefficient of determination R-squared close to 1) and good generalization ability. Materials were screened to retain those with band gaps between 0.1-3.0 eV and exclude radioactive element-containing compounds. The final predicted dataset comprises two parts: 54359 crystal structures from the Materials Project database and 26305 crystal structures discovered by Merchant et al. (2023 Nature 624 80). Ultimately, this study completed the prediction of shear modulus and bulk modulus for 80664 inorganic crystals. This work enriches existing material elastic data resources and provides robust support for material design, with all data openly available at https://doi.org/10.57760/sciencedb.j00213.00104.
Machine learning-guided synthesis of advanced inorganic materials
School of Materials Science and Engineering, Nanyang Technological University（南洋理工大学材料科学与工程学院）；School of Computer Science and Engineering, Nanyang Technological University（南洋理工大学计算机科学与工程学院）；State Key Laboratory of Heavy Oil Processing, China University of Petroleum (Beijing)（中国石油大学（北京）重质油国家重点实验室）；Centre for Micro-/Nano-electronics (NOVITAS), School of Electrical & Electronic Engineering, Nanyang Technological University（南洋理工大学电气与电子工程学院微/纳米电子中心（NOVITAS））；CINTRA CNRS/NTU/THALES, UMI 3288（法国国家科学研究中心-南洋理工大学-泰雷兹联合实验室（CINTRA UMI 3288））；Chemistry and Materials Centre, Nanyang Environment and Water Research Institute（南洋环境与水研究所化学与材料中心）
Scalable Dielectric Tensor Predictions for Inorganic Materials using Equivariant Graph Neural Networks
The Hong Kong Polytechnic University（香港理工大学）；The University of Tokyo（东京大学）；Hong Kong University of Science and Technology（香港科技大学）；The Chinese University of Hong Kong（香港中文大学）；National Institute for Materials Science（国立研究开发法人物质·材料研究机构）；RIKEN Center for Advanced Intelligence Project（理化学研究所先进智能研究中心）
The influence of Structural Dynamics in Two-Dimensional Hybrid Organic-Inorganic Perovskites on their Photoluminescence Efficiency -- Neutron scattering analysis
Mo-Re-W Alloys for High Temperature Applications: Phase Stability, Elasticity, and Thermal Property Insights via Multi-Cell Monte Carlo and Machine Learning
Accelerated design of linear-superelastic Ti-Nb nanocomposite alloys with ultralow modulus via high-throughput phase-field simulations and machine learning
Score-based diffusion models for accurate crystal-structure inpainting and reconstruction of hydrogen positions
Paul Scherrer Institute（保罗谢勒研究所）；Università di Parma（帕尔马大学）；INFN - Sezione di Milano-Bicocca（意大利国家核物理研究所米兰-比可卡分部）；Università degli Studi di Modena e Reggio Emilia（摩德纳和雷焦艾米利亚大学）；Institute Nanoscience - CNR-NANO, Center S3（纳米科学研究所-CNR-NANO，S3中心）
A New Workflow for Materials Discovery Bridging the Gap Between Experimental Databases and Graph Neural Networks
Department of Physics, University at Buffalo, State University of New York, Buffalo, NY；Department of Computer Science \& Engineering, University at Buffalo, State University of New York, Buffalo, NY；Institute for Artificial Intelligence；Data Science, University at Buffalo, State University of New York, Buffalo, NY
