New neural network demonstrates enhanced symmetry awareness

• Published in: American Ceramic Society. American Ceramic Society Bulletin Vol. 100; no. 9; p. 13
• Format: Trade Publication Article
• Language: English
• Published: Columbus American Ceramic Society 01.12.2021
• Subjects: Algorithms; Artificial intelligence; Computer techniques; Data science; Machine learning; Neural networks; Unstructured data
• ISSN: 0002-7812; 1945-2705
• DOI: 10.1038/s41524-021-00637-y

In the past 10 years, more and more researchers are harnessing data science techniques for materials discovery and design. Data science is an interdisciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insight from noisy, structured, and unstructured data. Instead of slowly identifying new materials through trial-and-error, researchers who adopt a data science framework use advanced computer techniques such as machine learning to quickly identify new materials worth further exploration. A challenge of using machine learning for materials design, however, is the need for a test data set on which to train the algorithm. Traditionally, research teams do not share their data with others, which makes accessing large amounts of data for training difficult. Yet even when data is available, it is rarely stored in a manner that can be used to train an algorithm.