Special Issue "Artificial Intelligence for Advanced Materials Research"
Deadline for manuscript submissions: closed (30 September 2021) | Viewed by 5547
Interests: materials testing and characterization; image analysis and classification; deep learning; artificial intelligence; computer vision; smart materials; materials in industry; innovation in materials
In the past decade, Artificial Intelligence (AI) has taken the world by storm. Deep Learning methods based on convolutional neural networks currently lead machine learning benchmarks in computer vision, speech recognition, machine game play, and various other fields. One of their key advantages consists in the power to process vast amounts of data to result in conclusions matching those of human experts. Such capabilities yield tremendous impact in a wide variety of fields. For example, the current trends suggest that medicine will be completely transformed over the next few years, given that AI tools embedded in smartphones, smartwatches or other wearables can contribute to the timely detection of silent but deadly pathologies and precursory signs, and will thus allow implementing timelier and more efficient therapeutic strategies compared to current ones. When it comes to materials science, AI makes it possible to replace traditional trial-and-error approaches with novel ones that provide the optimal solution for a given problem at the click of a mouse-button. AI tools help researchers synthetise new materials faster by intelligently optimizing the composition and extracting meaningful data from any type of measurement record: micrographs, time-lapse microscopy movies, spectra, plots, etc. From identifying and classifying defects to distinguishing complicated features or optimizing microstructures, AI allows researchers to perform an automated data analysis, without operator bias or any data analysis expertise. Furthermore, recent results suggest that not long from now, AI will augment the usefulness of advanced systems for characterization to the point where it will be possible to measure properties that lie outside the resolving power of any available hardware instrument (e.g., microscopes, spectrometers, etc.). Furthermore, AI methods for cross-modality imaging will enable large scale (virtual) access to latest-hour characterization tools, enabling novel research routes and perspectives at a scale that is still difficult to comprehend. This Special Issue welcomes original research articles presenting significant advances in the field of AI methods, applications, and case studies for the synthesis and characterization of advanced materials, as well as timely reviews and perspectives addressing key problems in the field.
Dr. Alisa Stratulat
Dr. Stefan G. Stanciu
Manuscript Submission Information
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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
- artificial intelligence
- advanced materials
- material synthesis
- material characterization
- material testing
- image analysis