Deep Learning in Computational Materials Science

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Background

Riding with the current wave of Artificial Intelligence (AI), many engineers and scientists have adopted machine learning and deep learning as powerful tools in various engineering disciplines, including materials science. Utilizing artificial neural networks, deep learning is one of the most effective, supervised, time-efficient, and cost-efficient machine learning approaches. It has been successfully used in various computational materials science research topics, such as developing potential functions for molecular dynamics, predicting the mechanics of materials, optimizing material and structural design, and promoting novel multiscale modeling and simulation. Moreover, deep learning enhances the data-driven approach in the materials science research community via big data analyses and image processing.

This special issue aims to bring researchers together to share the insights of AI in current research projects and promote the applications of deep learning in computational materials science at different spatial and temporal scales. Specifically, deep-learning-enhanced data-driven approach may provide an alternative solution to bridge nano, micro, and macro scales in novel multiscale methods. This can enhance the studies of materials' mechanical, thermal, and electrical behavior and, in turn, promote the design of new materials and composites.

The topics of interest to this symposium include, but are not limited to, the following:
• Review of the state-of-the-art deep learning methods: artificial neural network, convolutional neural network, recurrent neural network, etc. in the applications of computational materials science
• Artificial neural network based potential function for molecular simulations
• Machine learning and deep learning in multiscale modeling and simulations
• Other machine learning and AI approaches in computational materials science and engineering
• Applications in material and structure optimization design and nondestructive evaluation

Keywords: Deep learning, multiscale modeling, data-driven approach, Artificial Intelligence, Computational Materials Science and Engineering, Nondestructive Evaluation

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