Research Advances on the Role of Deep Learning in Materials InformaticsPages 192-205
Opeyemi S. Akanbi Abstract:
Deep materials informatics is a rapidly evolving field that employs deep learning techniques to develop predictive models for materials science. It involves the use of large datasets, advanced algorithms, and high-performance computing to extract key features from complex materials data. The aim of deep materials informatics is to speed up the process of locating materials with desired attributes, by taking advantage of machine learning. There are numerous applications of this technology, and it can forecast the properties of materials at all levels - from the atomic to the macroscopic. For instance, deep materials informatics are used to predict the electrical, thermal and mechanical characteristics of materials, which are critical for designing new materials for various applications. This paper offers a thorough examination of the fundamentals of deep learning, its benefits and drawbacks, and its current usage in the analysis of numerous materials datasets. Additionally, it can be utilized to optimize the processing parameters for creating materials with desired characteristics.
Keywords: Deep learning,
Deep materials informatics,
Neural networks,
Data-driven science
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