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Metals
Special Issues
Data-Driven Approaches in Modeling of Intermetallics
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Data-Driven Approaches in Modeling of Intermetallics

A special issue of Metals (ISSN 2075-4701). This special issue belongs to the section "Computation and Simulation on Metals".

Deadline for manuscript submissions: closed (30 April 2022) | Viewed by 5616

Special Issue Editor

Dr. Anil Kunwar

E-Mail Website
Guest Editor
Faculty of Mechanical Engineering, Silesian University of Technology, Konarskiego 18A, 44-100 Gliwice, Poland
Interests: finite element method; laser-material interaction; data-driven materials science; artificial neural network; Pb-free solder alloys; intermetallic compounds; multi-principal element alloys; dynamics at materials interface; multiphysics simulation; heat transfer; transport phenomena at mesoscale; in situ imaging techniques
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

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

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Metals is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Intermetallic Compounds (Intermetallics)
  • CALPHAD
  • Phase Field Method
  • Machine Learning
  • Artificial Neural Network
  • Optimization
  • Big Data
  • Metal-Intermetallics Interface
  • Inverse Design
  • Modeling

Published Papers (2 papers)

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Research

13 pages, 1256 KiB  
Article
Automatic Featurization Aided Data-Driven Method for Estimating the Presence of Intermetallic Phase in Multi-Principal Element Alloys
by Upadesh Subedi, Yuri Amorim Coutinho, Prafulla Bahadur Malla, Khem Gyanwali and Anil Kunwar
Metals 2022, 12(6), 964; https://doi.org/10.3390/met12060964 - 04 Jun 2022
Cited by 4 | Viewed by 2110
Abstract
Multi-principal element alloys (MPEAs) are characterized by a high-dimensional materials design space, and data-driven models can be considered as the best tools to describe the structure–property relationship in this class of materials. Predicting the prevalence of an intermetallic (IM) phase in a high-entropy [...] Read more.
Multi-principal element alloys (MPEAs) are characterized by a high-dimensional materials design space, and data-driven models can be considered as the best tools to describe the structure–property relationship in this class of materials. Predicting the prevalence of an intermetallic (IM) phase in a high-entropy alloy (HEA) regime of MPEAs has become a very important research direction recently. In this work, Automatic Featurization capability has been deployed computationally to extract composition and property features from the datasets of MPEAs. Data visualization has been performed, and through principal component analysis, the relative impacts of the input features on the two principal components have been specified. Artificial neural network is then trained upon the set of compostion, property and phase information features. A GUI interface is subsequently developed on top of the prediction model to enable the user-friendly computer environment for detection of the IM phase in a compositionally complex alloy. Full article
(This article belongs to the Special Issue Data-Driven Approaches in Modeling of Intermetallics)
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8 pages, 2334 KiB  
Article
Simulation for Cu Atom Diffusion Leading to Fluctuations in Solder Properties and Cu6Sn5 Growth during Multiple Reflows
by Min Shang, Chong Dong, Haoran Ma, Yunpeng Wang and Haitao Ma
Metals 2021, 11(12), 2041; https://doi.org/10.3390/met11122041 - 16 Dec 2021
Cited by 1 | Viewed by 2324
Abstract
The multiple reflows process is widely used in 3D packaging in the field of electronic packaging. The growth behavior of interfacial intermetallic compound (IMC) is more important to the reliability of solder joints. In this paper, experimental measurement combined with simulation calculation were [...] Read more.
The multiple reflows process is widely used in 3D packaging in the field of electronic packaging. The growth behavior of interfacial intermetallic compound (IMC) is more important to the reliability of solder joints. In this paper, experimental measurement combined with simulation calculation were preformed to investigate the evolution of Cu concentration in solders during multiple reflows, as well as its effects on the growth behavior of IMC and solder properties. The concentration of Cu in solder fluctuated, increasing with the increase of reflow times, which led to the fluctuation in the growth rate of the IMC. Furthermore, the Vickers hardness and melting point of the solder fluctuated during the multiple reflow processes due to the fluctuation in the Cu concentration. The data generated during this study could help to develop machine learning tools in relation to the study of interfacial microstructure evolution during multiple reflows. Full article
(This article belongs to the Special Issue Data-Driven Approaches in Modeling of Intermetallics)
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