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, also referred to as “descriptors”, 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, 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.

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Machine learning in materials informatics: recent applications and prospects

Plastic strain localization in metals: origins and consequences

The high strain rate dependence of the flow stress of metals and alloys is described from a dislocation mechanics viewpoint over a range beginning from conventional tension/compression testing through split Hopkinson pressure bar (SHPB) measurements to Charpy pendulum and Taylor solid cylinder impact tests and shock loading or isentropic compression experiment (ICE) results. Single crystal and polycrystal measurements are referenced in relation to influences of the crystal lattice structures and nanopolycrystal material behaviours. For body centred cubic (bcc) metals, the strain rate sensitivity (SRS) is in the yield stress dependence as compared with the face centred cubic (fcc) case of being in the strain hardening property. An important consequence is that an opposite ductility influence occurs for the tensile maximum load point strain that decreases with strain rate for the bcc case and increases with strain rate for the fcc case. Different hexagonal close packed (hcp) metals are shown to foll...

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High strain rate properties of metals and alloys

The (cleavage) strength of pre-cracked polycrystals

Pioneering research results reported in the early 1950’s by E. O. Hall and N. J. Petch on iron and steel materials have led to an expanded description of the grain size dependence of the complete stress­strain behavior of a wider range of materials and including assessments of other mechanical properties such as the ductile to brittle transition behavior and the hardness of materials, particularly, of nanocrystalline materials. The dislocation pile-up model that was presented originally for the inverse square root of grain diameter dependence of material strength has endured. Most recently, the pile-up model description has been more definitely associated with the Griffith theory of achieving a critical stress concentration at the tip of a crack; and, the Hall-Petch analysis has been connected to the macro-scale description of the fracture mechanics stress intensity parameter. These topics and other “60 years of Hall-Petch” type researches are tracked over time in the present report while giving special emphasis to current order-of-magnitude strength improvements that are reported for metals with nanopolycrystalline grain diameters.

https://www.jstage.jst.go.jp/article/matertrans/55/1/55_MA201302/_pdf

N.J. Petch

Papers

The influence of grain boundary carbide and grain size on the cleavage strength and impact transition temperature of steel

Work-hardening in cleavage fracture toughness

The influence of some substitutional alloys on the cleavage of ferritic steels