Understanding, discovery, and synthesis of 2D materials enabled by machine learning
Citations Over TimeTop 1% of 2022 papers
Abstract
Machine learning (ML) is becoming an effective tool for studying 2D materials. Taking as input computed or experimental materials data, ML algorithms predict the structural, electronic, mechanical, and chemical properties of 2D materials that have yet to be discovered. Such predictions expand investigations on how to synthesize 2D materials and use them in various applications, as well as greatly reduce the time and cost to discover and understand 2D materials. This tutorial review focuses on the understanding, discovery, and synthesis of 2D materials enabled by or benefiting from various ML techniques. We introduce the most recent efforts to adopt ML in various fields of study regarding 2D materials and provide an outlook for future research opportunities. The adoption of ML is anticipated to accelerate and transform the study of 2D materials and their heterostructures.
Related Papers
- Susquehanna Chorale Spring Concert "Roots and Wings"(2017)
- → DETERMINING QUALITY REQUIREMENTS AT THE UNIVERSITIES TO IMPROVE THE QUALITY OF EDUCATION(2018)
- Коммуникaтивно- прaгмaтический aнaлиз дипломaтических бумaг (нa основе вербaльных нот)(2018)
- → ФОРМИРОВAНИЕ ГОТОВНОСТИ БУДУЩИХ ПЕДAГОГОВ К ОРГAНИЗAЦИИ РAБОТЫ ПО РAЗВИТИЮ ВAЛЕОЛОГИЧЕСКОЙ КУЛЬТУРЫ ШКОЛЬНИКОВ(2023)