Materials and AI Research You Don’t Want to Miss

Stay informed about new advancements in this topic! View in Browser SUBMIT AN ARTICLE MANAGE MY ALERTS ABOUT THE JOURNAL Applied Physics Reviews High Impact Research in Materials and AI Applications of AI in materials science are leading to accelerations in materials discovery. Accelerated and accurate predictions of materials properties or increasing the speed and accuracy of on-the-fly data collection and analysis are excellent examples of how AI can improve many aspects of materials research.  Want to know more about this topic? We have a great article collection for you! Generate Buzz in Your Community Last year, APR articles were mentioned 1,155 times in the news and social media. SUBMIT AN ARTICLE > Intelligent meta-imagers: From compressed to learned sensing Chloé Saigre-Tardif, Rashid Faqiri, Hanting Zhao, Lianlin Li, et al. READ MORE > Discovering exceptionally hard and wear-resistant metallic glasses by combining machine-learning with high throughput experimentation Suchismita Sarker, Robert Tang-Kong, Rachel Schoeppner, Logan Ward, et al. READ MORE > Toward autonomous materials research: Recent progress and future challenges Joseph H. Montoya, Muratahan Aykol, Abraham Anapolsky, Chirranjeevi B. Gopal, et al. READ MORE > On-the-fly autonomous control of neutron diffraction via physics-informed Bayesian active learning Austin McDannald, Matthias Frontzek, Andrei T. Savici, Mathieu Doucet, et al. READ MORE > Materials representation and transfer learning for multi-property prediction Shufeng Kong, Dan Guevarra, Carla P. Gomes, John M. Gregoire READ MORE > Computational design of moiré assemblies aided by artificial intelligence Georgios A. Tritsaris, Stephen Carr, Gabriel R. Schleder READ MORE > Data-assisted polymer retrosynthesis planning Lihua Chen, Joseph Kern, Jordan P. Lightstone, Rampi Ramprasad READ MORE > Gryffin: An algorithm for Bayesian optimization of categorical variables informed by expert knowledge Florian Häse, Matteo Aldeghi, Riley J. Hickman, Loïc M. Roch, et al. READ MORE > Machine learning for materials discovery: Two-dimensional topological insulators Gabriel R. Schleder, Bruno Focassio, Adalberto Fazzio READ MORE > Modern nanoscience: Convergence of AI, robotics, and colloidal synthesis Robert W. Epps, Milad Abolhasani READ MORE > Constrained non-negative matrix factorization enabling real-time insights of in situ and high-throughput experiments Phillip M. Maffettone, Aidan C. Daly, Daniel Olds READ MORE > A deep learning augmented genetic algorithm approach to polycrystalline 2D material fracture discovery and design Andrew J. Lew, Markus J. Buehler READ MORE > Data-driven materials research enabled by natural language processing and information extraction Elsa A. Olivetti, Jacqueline M. Cole, Edward Kim, Olga Kononova, et al. READ MORE > Share on your social channels Follow us on social media   Copyright © 2022 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.