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Discover how machine learning accelerates materials discovery—from predictive modeling to nanoscale characterization View in Browser SUBMIT AN ARTICLE MANAGE MY ALERTS ABOUT THE JOURNAL APL Machine Learning Machine Learning Meets Materials Science Explore cutting-edge research where AI transforms materials design, characterization, and performance. Machine learning is rapidly reshaping the landscape of materials science, offering tools that go far beyond traditional modeling and experimental approaches. By leveraging data-driven algorithms, researchers can now predict properties, optimize compositions, and uncover hidden relationships in complex systems. This curated article collection from APL Machine Learning shows how AI is accelerating innovation across diverse areas of materials research. Explore how deep language models are being applied to interpret and predict material behaviors, and how Bayesian optimization is guiding the design of high-performance niobium alloys for extreme environments. Discover frameworks that predict elastic constants in multi-principal element alloys, and machine learning approaches that enhance nanoscale imaging through scanning probe microscopy. These advances demonstrate the growing synergy between machine learning and materials science—unlocking faster and smarter pathways to discovery. Publish Your Next Paper With AML Join the conversation shaping the future of materials research and contribute to this exciting frontier. SUBMIT YOUR RESEARCH > Machine learning guided optimal composition selection of niobium alloys for high temperature applications Trupti Mohanty, K. S. Ravi Chandran, Taylor D. Sparks READ MORE > A machine learning framework for elastic constants predictions in multi-principal element alloys Nathan Linton, Dilpuneet S. Aidhy READ MORE > Robust design of semi-automated clustering models for 4D-STEM datasets Alexandra Bruefach, Colin Ophus, M. C. Scott READ MORE > Deep language models for interpretative and predictive materials science Yiwen Hu, Markus J. Buehler READ MORE > Resistance transient dynamics in switchable perovskite memristors Juan Bisquert, Agustín Bou, Antonio Guerrero, Enrique Hernández-Balaguera READ MORE > Improved prediction for failure time of multilayer ceramic capacitors (MLCCs): A physics-based machine learning approach Pedram Yousefian, Alireza Sepehrinezhad, Adri C. T. van Duin, Clive A. Randall READ MORE > Scanning probe microscopy in the age of machine learning Md Ashiqur Rahman Laskar, Umberto Celano READ MORE > Accelerating defect predictions in semiconductors using graph neural networks Md Habibur Rahman, Prince Gollapalli, Panayotis Manganaris, Satyesh Kumar Yadav, et al. READ MORE > Improving the mechanical properties of Cantor-like alloys with Bayesian optimization Valtteri Torsti, Tero Mäkinen, Silvia Bonfanti, Juha Koivisto, et al. READ MORE > Machine learning based hybrid ensemble models for prediction of organic dyes photophysical properties: Absorption wavelengths, emission wavelengths, and quantum yields Kapil Dev Mahato, S. S. Gourab Kumar Das, Chandrashekhar Azad, Uday Kumar READ MORE > FURTHER MACHINE LEARNING AND MATERIALS RESEARCH Read, cite, and share more materials science research from our machine learning portfolio. Accurate prediction of dielectric properties and bandgaps in materials with a machine learning approach Yilin Hu, Maokun Wu, Miaojia Yuan, Yichen Wen, et al. Appl. Phys. Lett. READ MORE > Highly accurate classification of material types from spectroscopic ellipsometry heatmap measurements using deep learning Masahiro Hayashi, James N. Hilfiker, Takuji Maekawa, Hitoshi Tampo, et al. Appl. Phys. Lett. READ MORE > Interpretable machine learning for materials discovery: Predicting CO2 adsorption properties of metal–organic frameworks Yukun Teng, Guangcun Shan APL Mater. READ MORE > Machine learning prediction of thermodynamic stability and electronic properties of 2D layered conductive metal–organic frameworks Zeyu Zhang, Yuliang Shi, Farnaz A. Shakib APL Mater. READ MORE > Tell Us What You Think! Did you like this email? Yes > No >  Follow us on social media   Copyright © 2025 AIP Publishing. All rights reserved. 1305 Walt Whitman Rd., Melville, NY 11747 You are receiving this email because you have opted-in to receive alerts from us. To guarantee delivery of this email please add journals@aip-info.org to your address book and safe senders list. If you no longer wish to receive emails from us then please unsubscribe or amend your settings. Privacy Policy