Material structure-property linkages using three-dimensional convolutional neural networks

By Ahmet Cecen, Hanjun Dai, Y. Yabansu, Surya R. Kalidindi, Le Song. 2018

299 citations recorded by the source corpus.

Subfields: Materials Chemistry, Mechanical Engineering

View Paper: https://www.semanticscholar.org/paper/22a4c140e6ab21225dcf50624977164b8b0742ac

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Materials Chemistry

1. Shyue Ping Ong
2. Chi Chen
3. Rohit Batra
4. Anubhav Jain
5. Rampi Ramprasad
6. Alok N. Choudhary
7. Chris Wolverton
8. Le Song

In Mechanical Engineering

1. Rohit Batra
2. Chi Chen
3. Le Song
4. Chris Wolverton
5. Shyue Ping Ong
6. Rampi Ramprasad

Citations in the corpus

• Using deep neural network with small dataset to predict material defects (2019, background): “3 Potential for applying DNN with small dataset Although convolution neural network for microstructure image recognition [43-45] and variational auto-encoder for text mining [46] have been introduced into material science, the applications of DNNs in material science are still limited.”
• Emerging materials intelligence ecosystems propelled by machine learning (2020, background): “The reverberation of the impact of CNNs is also felt in materials science, especially in the context of image segmentation 97 , fault and failure detection 98 , learning microstructure–property relations 99 and developing property-prediction models 100 , among other examples 101 .”
• Recent advances and applications of deep learning methods in materials science (2021, background): “, predicting the macroscale property of a material given its microstructure information [259, 261, 285], as well as for localization, i.”