Materials informatics for the design of novel coatings

doi:10.1016/j.surfcoat.2005.08.097

Surface and Coatings Technology

Volume 200, Issues 5–6, 21 November 2005, Pages 1595-1599

https://doi.org/10.1016/j.surfcoat.2005.08.097 Get rights and content

Abstract

Rational selection of materials in coating design, to achieve desired properties and performance, remains as a scientific challenge. Although the empirical trial-and-error approach is widely used in coating fabrication, modeling-based methods, such as first-principles calculation, need to be explored and developed. In this paper, we present a novel approach for the design of hard coatings using elastic properties of transition-metal nitrides calculated from first-principles density functional theory. The hardness and ductility trends in these materials are identified using physical and empirical criteria, and validated with experimental observations. Applications of the property trends to materials selection for hard coatings demonstrate that this approach can be used, not only to interpret existing experimental observations, but also to predict new materials with the desired properties for coating design.

Introduction

The design of novel coatings involves rational selection of material constituents and development of fabrication processes to produce desired microstructures. Several aspects of materials selection for hard coatings have been discussed by Holleck [1], elucidating how chemical bonding and microstructure can influence coating properties. Similar design concepts can be applied to nanostructured hard coatings. For instance, to produce nanolayered superlattice coatings with appreciable hardness enhancement, the layer materials should be chosen in such a way that they exhibit a large difference in shear modulus (ΔG). When the superlattice period is below a certain threshold, which is a function of the layer materials, a large ΔG will allow the layer interfaces to act as effective barriers to dislocation propagation from the softer layers to the harder layers under mechanical loading [2], [3], [4]. For certain metal-nitride superlattices with large ΔG, such as TiN/VN and TiN/NbN, hardness values much higher than those described by the “rule of mixture” are achievable; on the other hand, little hardness enhancement is observed in superlattice VN/NbN having very small ΔG [4]. Materials selection plays an equally important role in the design of nanocomposite hard coatings, although it appears not as straightforward as for superlattice coatings. In a recent review [5], Veprek has analyzed the design criteria to produce superhard or even ultrahard nanocomposite coatings. From a materials selection point of view, combinations of nanocrystalline transition-metal nitrides, e.g. TiN, W₂N and VN, with amorphous Si₃N₄ or BN as the grain boundary phase, have a potential to achieve coating hardness exceeding 40–50 GPa. This hardening behavior, particularly in the regime where the crystallite size d ≲ 10 nm, arises from effective retardation of grain boundary sliding by the pinning effect of the amorphous phase. It is worth noting that the fracture strength, and thus the hardness of superhard nanocomposite coatings is proportional to the Young's modulus of the material constituents, in which nanoscale defects are formed and dominate mechanical failure of the coatings.

Materials selection for coating design could be made empirically through trial-and-error experiments, or by a combination of analytical studies with rationalized experimentation. In the latter approach, one could utilize the patterns or trends identified in property databases with “data-mining” techniques to navigate materials selection. Materials data can be derived from experiments, calculation or a combination of the two. This approach, known as “materials informatics” [6], has found useful applications for materials design. In this paper, first-principles calculations of elastic properties for selected transition-metal nitrides are described. The procedure used to estimate hardness and ductility trends in these materials using physical and empirical criteria is also provided. Finally, a demonstration of how to select materials for coating design based on the estimated property trends is presented.

Section snippets

Elastic properties calculation

Table 1 lists the binary and ternary materials investigated. The majority of the materials are IV–VI group transition-metal nitrides with a cubic (c) B1 structure. AlN is a covalent metal nitride with a hexagonal (h) B4 structure. The elastic coefficients (C_ij) of the single crystal, and the elastic moduli of the polycrystalline single-phase materials, were calculated from first-principles density functional theory (DFT) using atomic unit-cell models. The calculations were performed using the

Data mining

From a coating-design perspective, identifying the patterns or trends, in materials property data, can provide useful guidance for materials selection. To demonstrate this concept, we present the materials listed in Table 1 as a function of calculated bulk modulus (B) and shear modulus (G) in Fig. 1. Several data points are labeled with corresponding materials. The relationships between hardness and elastic moduli for hard materials, including metal nitrides, have been discussed in Refs. [12],

Materials selection for coating design

There are experimental data that appear to support the predicted hardness trend from this study. For example, the Vickers hardness for binary metal nitrides TiN, ZrN, VN, NbN, AlN and CrN, presented in Tables 1 and 2 of Ref. [1], is proportional to the calculated shear modulus shown in Table 3 of this paper (Fig. 3). Measuring the ductility of metal nitrides, particularly in the form of thin-film coatings, is not as trivial as measuring the hardness of these materials, and thus the predicted

Summary

This paper describes the concept of using materials informatics approach for the design of coatings with desired properties. The process includes three major components: (1) assembling material properties data, (2) analyzing property trends or patterns using data-mining techniques, and (3) selecting rationally materials from the property trends for coating design. Several potential applications of this approach are demonstrated through the design of hard coatings using calculated elastic

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Materials informatics for the design of novel coatings

Abstract

Introduction

Section snippets

Elastic properties calculation

Data mining

Materials selection for coating design

Summary

J. Less-Common Met.

Scr. Mater.

Surf. Coat. Technol.

J. Vac. Sci. Technol., A

Phys. Rev. B

J. Appl. Phys.

J. Appl. Phys.

J. Vac. Sci. Technol., A

AMPTIAC Newsl.