Machine Learning and Materials Informatics: Recent Applications and Prospects

By Rampi Ramprasad, Rohit Batra, Ghanshyam Pilania, A. Mannodi‐Kanakkithodi, Chiho Kim. 2017

708 citations recorded by the source corpus.

Propelled partly by the Materials Genome Initiative, and partly by the algorithmic developments and the resounding successes of data-driven efforts in other domains, informatics strategies are beginning to take shape within materials science. These approaches lead to surrogate machine learning models that enable rapid predictions based purely on past data rather than by direct experimentation or by computations/simulations in which fundamental equations are explicitly solved. Data-centric informatics methods are becoming useful to determine material properties that are hard to measure or compute using traditional methods--due to the cost, time or effort involved--but for which reliable data either already exists or can be generated for at least a subset of the critical cases. Predictions are typically interpolative, involving fingerprinting a material numerically first, and then following a mapping (established via a learning algorithm) between the fingerprint and the property of interest. Fingerprints may be of many types and scales, as dictated by the application domain and needs. Predictions may also be extrapolative--extending into new materials spaces--provided prediction uncertainties are properly taken into account. This article attempts to provide an overview of some of the recent successful data-driven "materials informatics" strategies undertaken in the last decade, and identifies some challenges the community is facing and those that should be overcome in the near future.

Subfields: Computational Theory and Mathematics, Materials Chemistry

View Paper: https://www.semanticscholar.org/paper/91f7848ea9045f29345467552496db97b037ae01

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In Computational Theory and Mathematics

1. Qizhi He
2. Ryan Jacobs
3. Jiun-Shyan Chen
4. Dane Morgan

In Materials Chemistry

1. Ryan Jacobs
2. Dane Morgan
3. Jiun-Shyan Chen

Citations in the corpus

• A Physics-Constrained Data-Driven Approach Based on Locally Convex Reconstruction for Noisy Database (2019, methodology): “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].”