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Digital Discovery, 2025, 4, 625
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Comparative assessment of composition- and structure-based surrogate models across 2D materials databases

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Phys. Chem. Chem. Phys., 2026, 28, 10119
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Convergence of high throughput experimentation and machine learning to rapidly advance application-specific polymer development

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A critical examination of active learning workflows in materials science

Akhil S. Nair and Lucas Foppa

Digital Discovery, 2026
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Physical encoding improves OOD performance in deep learning materials property prediction

Nihang Fu, Sadman Sadeed Omee and Jianjun Hu

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Out-of-distribution machine learning for materials discovery: Challenges and opportunities

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Chemical Physics Reviews, 2026, 7
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Machine Learning and Domain Adaptation for Material Generalization in Electrocatalyst Evaluation: A Comprehensive Review

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Arch Computat Methods Eng, 2026
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Prediction and investigation of transition metal oxides for hydrogen storage application via machine learning-assisted density functional theory

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J Nanopart Res, 2026, 28
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Active learning enables generation of molecules that advance the known Pareto front

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npj Comput Mater, 2026, 12
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Deep learning-assisted prediction of electric field impact on impurity photoionization cross section in CdS/ZnSe core/shell spherical quantum dot embedded in PVA matrix

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Critical review on in silico methods for structural annotation of chemicals detected with LC/HRMS non-targeted screening

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Capturing variability in material property predictions for plastics recycling via machine learning

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Digital Chemical Engineering, 2025, 15, 100239
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Sensitivity and Performance Analysis of an AI Model for Virtual Prototyping of Brazed Ceramic–Metal Composite Materials

Sunita Khod, Vinay Kamma, Ravi Kumar Varma and Mayank Goswami

Int J Applied Ceramic Tech, 2026, 23
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Accelerated prediction of temperature-dependent lattice thermal conductivity via ensembled machine learning models

Piyush Paliwal and Aftab Alam

Phys. Rev. Materials, 2025, 9
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Out-of-Distribution Material Property Prediction Using Adversarial Learning

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Artificial intelligence-driven methodology for predicting brazed ceramic–metal composite materials

Sunita Khod, Vinay Kamma, Ravi Kumar Varma and Mayank Goswami

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Improved hardness prediction for reduced-activation high-entropy alloys by incorporating symbolic regression and domain adaptation on small datasets

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Machine-Learning-Guided Discovery and Statistical Analysis of Multielemental Reverse Water–Gas Shift Catalysts

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ACS Catal., 2026, 16, 5080
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Optical and electronic properties of RENiO3 doped with alkali earth metals (Be, Mg, Ca, Sr and Ba): Combining first principles calculations and machine learning

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Hybrid genetic algorithm with interpretable surrogate modeling for enhanced optimization of short-fiber reinforced polymer composites

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

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

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Adaptive learning algorithm based price prediction model for auction lots—deep clustering based interval quoting

Da Ke, Xianhua Fan and Muhammad Asif

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Prediction and inverse design of bandgaps in acoustic metamaterials using deep learning and metaheuristic optimization techniques

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LiClustCondAI: A clustering-based ensemble regression framework for ionic conductivity predictions in solid-state electrolytes

Bijan Paul, Samarpan Mohanty, Hongzhi Guo and Mona Bavarian

Journal of Power Sources, 2026, 678, 240078
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MD-HIT: Machine learning for material property prediction with dataset redundancy control

Qin Li, Nihang Fu, Sadman Sadeed Omee and Jianjun Hu

npj Comput Mater, 2024, 10
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Hydrogen permeability prediction in palladium alloys and virtual screening of B2 phase-stabilized Pd(100-x-y)CuxMy ternary alloys using machine learning

Eric Kolor, Edoardo Magnone, Muhammad Harussani Moklis, Md. Rubel, Sasipa Boonyubol, Koichi Mikami and Jeffrey S. Cross

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Machine Learning to Adaptively Predict Gold Nanorod Sizes on Different Substrates

Katsuya Shiratori, Claire A. West, Zhenyang Jia, Stephen A. Lee, Emily A. Cook, Catherine J. Murphy, Christy F. Landes and Stephan Link

J. Phys. Chem. C, 2025, 129, 5913
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Transforming Computational Nanotechnology: Accelerating Material Discovery, Design, and Property Prediction through Soft Computing Techniques

Hirendra Das and Mridusmita Sharma

ACS Appl. Electron. Mater., 2025, 7, 5757
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Accelerating structure relaxation in chemically disordered materials with a chemistry-driven model

Liying An, Huan Ma, Jinjia Liu, Wenping Guo and Xiaodong Wen

npj Comput Mater, 2025, 11
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Material-embedding Physics-Augmented Neural Networks: A first application to constitutive law parameterization

C. Jailin and E. Baranger

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Machine learning in magnetism: Progress, challenges, and perspectives

Chao Jiang, Weifeng Xie, Min Wang, Tiege Zhou, Wendong Song, Yunliang Yue and Xuelong Hu

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