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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\u2014due to the cost, time or effort involved\u2014but 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, also referred to as \u201cdescriptors\u201d, may be of many types and scales, as dictated by the application domain and needs. Predictions may also be extrapolative\u2014extending into new materials spaces\u2014provided prediction uncertainties are properly taken into account. This article attempts to provide an overview of some of the recent successful data-driven \u201cmaterials informatics\u201d strategies undertaken in the last decade, with particular emphasis on the fingerprint or descriptor choices. The review also identifies some challenges the community is facing and those that should be overcome in the near future.<\/jats:p>","DOI":"10.1038\/s41524-017-0056-5","type":"journal-article","created":{"date-parts":[[2017,12,7]],"date-time":"2017-12-07T15:36:16Z","timestamp":1512660976000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1601,"title":["Machine learning in materials informatics: recent applications and prospects"],"prefix":"10.1038","volume":"3","author":[{"given":"Rampi","family":"Ramprasad","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rohit","family":"Batra","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ghanshyam","family":"Pilania","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Arun","family":"Mannodi-Kanakkithodi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chiho","family":"Kim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2017,12,13]]},"reference":[{"key":"56_CR1","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1038\/scientificamerican0617-60","volume":"316","author":"A Gopnik","year":"2017","unstructured":"Gopnik, A. 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