Materials informatics

By Seeram Ramakrishna, Tong-Yi Zhang, W. Lu, Q. Qian, J. Low, Jeremy H. Yune, Daren Zong Loong Tan, S. Bressan, Stefano Sanvito, Surya R. Kalidindi. Journal of Intelligent Manufacturing, 2018

338 citations recorded by the source corpus.

Subfields: Automotive Engineering, Industrial and Manufacturing Engineering, Materials Chemistry

View Paper: https://www.semanticscholar.org/paper/57f048e9f687c790051c7e2195270a1a89f2edb0

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. Magdalena Ortiz
2. Ronald E. Miller
3. Ellad B. Tadmor

Citations in the corpus

• Data-driven computational mechanics (2015, positive): “Material informatics uses database techniques to first identify parameters of correlation and then use machine-learning regression techniques [1] to ultimately provide predictive quantitative models [2].”
• Applying Neural-Network-Based Machine Learning to Additive Manufacturing: Current Applications, Challenges, and Future Perspectives (2019, background): “In many material areas, researchers have developed wellknown databases for organizing/storing/accessing data electronically, such as MatWeb, OQMD, and Citrine [68].”
• Theory-Guided Machine Learning in Materials Science (2016, neutral): “Materials informatics seeks to establish structure–property relationships in a high-throughput, statistically robust, and physically meaningful manner (Lookman et al., 2016; Rajan, 2005).”
• A Physics-Constrained Data-Driven Approach Based on Locally Convex Reconstruction for Noisy Database (2019, neutral): “The data-driven approaches, such as machine learning [5,6], have been widely applied to computational biological [7] and medical diagnosis [8], material informatics [9,10], and other predictive physics problems [4,11].”