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Abstract
In recent years, the advent of machine learning (ML) in materials science has provided a new tool for accelerating the design and discovery of new materials with a superior combination of mechanical properties for structural applications. In this review, we provide a brief overview of the current status of the ML-aided design and development of metallic alloys for structural applications, including high-performance copper alloys, nickel- and cobalt-based superalloys, titanium alloys for biomedical applications and high strength steel. We also present our perspectives regarding the further acceleration of data-driven discovery, development, design and deployment of metallic structural materials and the adoption of ML-based techniques in this endeavor.
Highlights
Materials have propelled the development of society over the course of history[1]
All above machine learning (ML) studies for different classes of materials have common goals, namely, exploiting the collected materials datasets for ML model development, rapidly estimating the properties of hundreds to thousands of new candidate materials using the trained ML model and virtually screening promising candidates for experimental validation. This has led to the accelerated materials discovery, with successful stories ranging from high-strength Cu alloys, Ni- and Co-based superalloys, low-modulus and low-cost Ti alloys to highstrength steels and other classes of materials not covered in this review
It is noteworthy that many different algorithms exist, such as linear regressions, support vector machines, random forests (RFs) and neural network (NN), to name just a few
Summary
Materials have propelled the development of society over the course of history[1]. Nowadays, it has been recognized that the rate of materials advances to a large extent underpins the rate at which our modern society advances. The search for new or alternative materials with appropriate processing routes, whether through experiments or simulations, is often a slow and arduous task. The identification of correlations among the composition-processing-structure-property (CPSP) relationships in such a fashion has often relied on an iterative, labor-intensive process of trial and error[2]. Such processes are reliable but, expensive and slow (sometimes may take up to even decades), and the ability of researchers to develop new materials on a reasonable timeline has been severely limited
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