Materials 4.0: Materials big data enabled materials discovery
By R. Jose, Seeram Ramakrishna. 2018
145 citations recorded by the source corpus.
Subfields: Industrial and Manufacturing Engineering, Materials Chemistry, Surfaces, Coatings and Films
View Paper: https://www.semanticscholar.org/paper/24e59efc95e1a3e2c007c04369037d73ac7d8fc4
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Industrial and Manufacturing Engineering
In Materials Chemistry
- Ryan Jacobs
- Dane Morgan
- Surya R. Kalidindi
- Seeram Ramakrishna
- Stefano Sanvito
- Franz Roters
- Dierk Raabe
- Philip Eisenlohr
In Surfaces, Coatings and Films
Citations in the corpus
- Assessing relations between Circular Economy and Industry 4.0: a systematic literature review (2019, background): “BDA, instead, was seen as a good solution for either developing automated approaches assessing potential value pathways for secondary materials (Davis, Aid, and Zhu 2017; Jose and Ramakrishna 2018) or discovering potential industrial symbioses (Song et al. 2017).”
- Materials informatics (2018, background): “We refer to such a holistic approach as materials informatics (Rajan and Seeram 2018).”
- Opportunities and Challenges for Machine Learning in Materials Science (2020, methodology): “…et al. 2018 199 How to optimize materials and devices via design of experiments and machine learning: Demonstration using organic photovoltaics Jose and Ramakrishna 2018 200 Materials 4.0: Materials big data enabled materials discovery Xu 2018 201 Accomplishment and challenge of…”