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Victor Trinquet, Matthew L. Evans, Cameron J. Hargreaves, Pierre-Paul De Breuck and Gian-Marco Rignanese

Faraday Discuss., 2025, 256, 459
DOI: 10.1039/D4FD00092G

Machine learning-assisted design of oxygen-containing inorganic coating materials on a separator for lithium metal anodes

Chenxi Xu, Teng Zhao, Ji Qian, Ke Wang, Tianyang Yu, Wangming Tang, Li Li, Feng Wu and Renjie Chen

Inorg. Chem. Front., 2025, 12, 4032
DOI: 10.1039/D5QI00111K

The carbon cost of materials discovery: Can machine learning really accelerate the discovery of new photovoltaics?

Matthew Walker and Keith T. Butler

Mater. Horiz., 2026
DOI: 10.1039/D5MH01404B

Symmetry-informed graph neural networks for carbon dioxide isotherm and adsorption prediction in aluminum-substituted zeolites

Marko Petković, José-Manuel Vicent Luna, Elīza Beate Dinne, Vlado Menkovski and Sofía Calero

J. Mater. Chem. A, 2025, 13, 37018
DOI: 10.1039/D5TA02482J

Accelerating the discovery of high-performance nonlinear optical materials using active learning and high-throughput screening

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J. Mater. Chem. C, 2025, 13, 18197
DOI: 10.1039/D5TC01335F

Discovery of highly anisotropic dielectric crystals with equivariant graph neural networks

Yuchen Lou and Alex M. Ganose

Faraday Discuss., 2025, 256, 255
DOI: 10.1039/D4FD00096J

Multimodal learning of heat capacity based on transformers and crystallography pretraining

Hongshuo Huang and Amir Barati Farimani

Journal of Applied Physics, 2024, 135
DOI: 10.1063/5.0201755

Toward Stable Zinc Anode: An AI‐Assisted High‐Throughput Screening of Electrolyte Additives for Aqueous Zinc‐Ion Battery

Guangsheng Xu, Yue Li, Junfeng Li, Jinliang Li, Xinjuan Liu, Chenglong Wang, Wenjie Mai, Guang Yang and Likun Pan

Angew Chem Int Ed, 2025, 64
DOI: 10.1002/anie.202511389

Compositionally restricted atomistic line graph neural network for improved thermoelectric transport property predictions

Zeyu Wang, Run Hu, Xiaobing Luo and Jinlong Ma

Journal of Applied Physics, 2024, 136
DOI: 10.1063/5.0226327

Machine learning-driven elasticity prediction in advanced inorganic materials via convolutional neural networks

Yujie LIU, Zhenyu WANG, Hang LEI, Guoyu ZHANG, Jiawei XIAN, Zhibin GAO, Jun SUN, Haifeng SONG and Xiangdong DING

Acta Phys. Sin., 2025, 74, 120702
DOI: 10.7498/aps.74.20250127

Prediction and characterization of chemically complex σ−phase intermetallics with graph neural network

Wenhao Zhang, Mariano Forti, Runan Xie, Céline Barreteau, Taichi Abe, Jean-Marc Joubert, Thomas Hammerschmidt and Jean-Claude Crivello

Acta Materialia, 2025, 300, 121427
DOI: 10.1016/j.actamat.2025.121427

The Black Hole Strategy: Gravity-Based Representative Sampling for Frugal Graph Learning on Metal–Organic Framework Networks

Mehrdad Jalali, A. D. Dinga Wonanke, Pascal Friederich and Christof Wöll

J. Chem. Inf. Model., 2025, 65, 10885
DOI: 10.1021/acs.jcim.5c01518

Material innovation and AI-driven smart packaging for fruit and vegetable precision preservation: Applications and future trends

Haiyu Du, Yuqian Jiang, Aliya Simayi, Chaozhe Li, Yuanyuan Liu, Yongqiang Wen and Yao Tang

Trends in Food Science & Technology, 2026, 167, 105441
DOI: 10.1016/j.tifs.2025.105441

Matini-Net: Versatile Material Informatics Research Framework for Feature Engineering and Deep Neural Network Design

Myeonghun Lee, Taehyun Park and Kyoungmin Min

J. Chem. Inf. Model., 2024, 64, 8770
DOI: 10.1021/acs.jcim.4c01676

Descriptors for the interpretability of cubic perovskite bulk modulus oriented towards soft lattice screening

Jinming JIANG, Qingde SUN and Weibing ZHANG

Acta Phys. Sin., 2025, 74, 170201
DOI: 10.7498/aps.74.20250652

PINK: physical-informed machine learning for lattice thermal conductivity

Yujie Liu, Xiaoying Wang, Yuzhou Hao, Xuejie Li, Jun Sun, Turab Lookman, Xiangdong Ding and Zhibin Gao

J. Mater. Inf., 2025, 5
DOI: 10.20517/jmi.2024.86

Deep learning for symmetry classification using sparse 3D electron density data for inorganic compounds

Seonghwan Kim, Byung Do Lee, Min Young Cho, Myoungho Pyo, Young-Kook Lee, Woon Bae Park and Kee-Sun Sohn

npj Comput Mater, 2024, 10
DOI: 10.1038/s41524-024-01402-7

Toward Stable Zinc Anode: An AI‐Assisted High‐Throughput Screening of Electrolyte Additives for Aqueous Zinc‐Ion Battery

Guangsheng Xu, Yue Li, Junfeng Li, Jinliang Li, Xinjuan Liu, Chenglong Wang, Wenjie Mai, Guang Yang and Likun Pan

Angewandte Chemie, 2025, 137
DOI: 10.1002/ange.202511389

Crystal hypergraph convolutional networks

Alexander J. Heilman, Weiyi Gong and Qimin Yan

npj Comput Mater, 2025, 11
DOI: 10.1038/s41524-025-01826-9

Ionic species representations for materials informatics

Anthony Onwuli, Keith T. Butler and Aron Walsh

APL Machine Learning, 2024, 2
DOI: 10.1063/5.0227009

Optical spectra prediction using three-body information

Malte Grunert, Max Großmann and Erich Runge

Applied Physics Letters, 2026, 128
DOI: 10.1063/5.0305586

DenseGNN: universal and scalable deeper graph neural networks for high-performance property prediction in crystals and molecules

Hongwei Du, Jiamin Wang, Jian Hui, Lanting Zhang and Hong Wang

npj Comput Mater, 2024, 10
DOI: 10.1038/s41524-024-01444-x

Decoding active sites in high-entropy catalysts via attention-enhanced model

Liang Yin, Tiantian Ma, Zibo Zhu, Zhanao Yao, Songlin Yu, Yi Li, Chengbo Li, Nian Ran, Wei Zhou and Jianjun Liu

Sci. Adv., 2026, 12
DOI: 10.1126/sciadv.aea1170

Materials discovery acceleration by using conditional generative methodology

Caiyuan Ye, Yuzhi Wang, Xintian Xie, Tiannian Zhu, Jiaxuan Liu, Yuqing He, Lili Zhang, Junwei Zhang, Zhong Fang, Lei Wang, Zhipan Liu, Hongming Weng and Quansheng Wu

npj Comput Mater, 2025, 12
DOI: 10.1038/s41524-025-01930-w

Atoms as words: A novel approach to deciphering material properties using NLP-inspired machine learning on crystallographic information files (CIFs)

Lalit Yadav

AIP Advances, 2024, 14
DOI: 10.1063/5.0187741

Physical encoding improves OOD performance in deep learning materials property prediction

Nihang Fu, Sadman Sadeed Omee and Jianjun Hu

Computational Materials Science, 2025, 248, 113603
DOI: 10.1016/j.commatsci.2024.113603

Accelerating material property prediction using generically complete isometry invariants

Jonathan Balasingham, Viktor Zamaraev and Vitaliy Kurlin

Sci Rep, 2024, 14
DOI: 10.1038/s41598-024-59938-z

Structure-based out-of-distribution (OOD) materials property prediction: a benchmark study

Sadman Sadeed Omee, Nihang Fu, Rongzhi Dong, Ming Hu and Jianjun Hu

npj Comput Mater, 2024, 10
DOI: 10.1038/s41524-024-01316-4

POAT: Material property prediction from limited data via Pointwise Distance Distribution and Offset Attention

Zexi Yang, Qi Yu, Yapeng Zhan, Boran Li and Jiying Liu

Computational Materials Science, 2025, 258, 114104
DOI: 10.1016/j.commatsci.2025.114104

Accurate formation enthalpies of solids using reaction networks

Rasmus Fromsejer, Bjørn Maribo-Mogensen, Georgios M. Kontogeorgis and Xiaodong Liang

npj Comput Mater, 2024, 10
DOI: 10.1038/s41524-024-01404-5