Data-Driven Approaches in Modeling of Intermetallics

Dear Colleagues,

Intermetallic compounds (IMCs) find potential applications in broad areas, including but not limited to joining, energy devices, shape memory alloys, superconductors, catalysts, thermoelectrics, design study of multi-principal element alloys etc. The multitudes of applications and usefulness of  intermetallics  is attributed to their presence in diverse compositions and complex structures.  More than 20,000 IMCs existing in over 2100 structure types have been discovered by the scientists, and this database is being expanded continuously. The uncertainty associated in IMCs structure/composition prediction from arbitrary set of combining base metals, and  opacity of adjacent metallic microstructures  are the major challenges related to IMC study with experimental approach (1st paradigm) and/or theoretical approach (2nd paradigm) alone.

Computational Science (popular since 1950s) and big data-driven Science (trending since early 2000s) being respectively the 3rd and 4th paradigms of materials science; can remarkably enable the robust design and discovery of intermetallic compounds. Mesoscale phase field method (PFM) based simulation  built on top of CALPHAD database is a very relevant computational science approach to describe spatio-temporal behavior of intermetallic microstructure, and provides mechanistic understanding of  IMC evolution phenomena.  Machine learning models , when implemented on (big) data obtained from 1st, 2nd and/or 3rd paradigms of materials science, introduce the multivariate modeling of intermetallic compounds and therefore assist in  elimination of all the barriers associated with the design and discovery of IMC materials.

Therefore it is necessary to design the data-driven approaches in modeling of intermetallics for the purpose of (i) dissemination of  information about the structure, properties and behaviors of existing IMCs, and (ii) enabling the discovery of new IMCs through establishment of inductive inferences on this category of materials. This Special Issue is aimed at recent advances in data-driven methods as applied to intermetallics, including the aspects of machine learning and/or CALPHAD-based phase field models.

Dr. Anil Kunwar
Guest Editor

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