Machine learning in materials informatics: recent applications and prospects

Abstract 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…

• AV
  Alexander von Humboldt-Stiftung
• OO
  Office of Naval Research

  Awards: N00014-14-1-0098, N00014-16-1-2580, N00014-10-1-0944, N00014