Balancing Mechanical Properties and Sustainability in the Search for Superhard Materials

Abstract

The development of superhard materials is focused on two very different classes of compounds. The first contains only light, inexpensive main group elements and requires high pressures and temperatures for preparation whereas the second class combines a transition metal with light main group elements and in general tends to only need high reaction temperatures. Although the preparation conditions are simpler, the second class of compounds suffers from the transition metals used being expensive and exceedingly scarce. Thus, in the search for novel superhard compounds, synthetic accessibility, resource considerations, and material response must be balanced. The research presented here develops high-information density plots drawn from high-throughput first-principle calculations and data mining to reveal the optimal composition space to synthesize new materials. This contribution includes analysis of the experimentally known Vickers hardness for materials as well as screening over 1100 compounds from first-principle calculations to predict their intrinsic hardness. Both data sets are analyzed not only for their mechanical performance but also the compositional scarcity, and Herfindahl-Hirschman index is calculated. Following this methodology, it is possible to ensure targeted materials are not only sustainable and accessible but that they will also have superb mechanical response.

Finding the Next Superhard Material through Ensemble Learning

“…[ 13–16 ] For example, finding proportionality coefficients that relate H V to different combinations of the elastic moduli have produced several semi‐empirical hardness models with varying accuracy. [ 17–21 ] Nevertheless, constructing simple mathematical models remains insufficient to distinguish the multidimensional relationship between chemical composition, crystal structure, microstructure, and hardness. More recently, machine‐learning methods, which can capture such complex connections, have been created to identify new superhard materials based on the elastic moduli.…”

Machine Learning Directed Search for Ultraincompressible, Superhard Materials

“…One area where ML may also be useful is in the search for materials with exceptional mechanical properties, such as high incompressibility or extreme hardness. Traditionally, the search for new superhard materials, which by convention have a Vickers hardness ( H V ) > 40 GPa, has relied on trial-and-error methods or simple design rules . These researches have largely concentrated on discovering materials that form strong covalent bonds using light main group elements as in diamond, c -BN, B 6 O, and c -BC 2 N or identifying compounds that combine light elements and transition metals with high valence electron density like in ReB 2 and WB 4 . − While the first class benefits from relying on abundant, cheap elements, their synthesis requires extreme pressures and temperatures, making their preparation cumbersome.…”

Cross-sectional transmission electron microscopy studies of boron ion implantation in hexagonal boron nitride

“…Cubic BN is an important material because of its properties such as super hardness 75 GPa [6][7][8] high thermal stability for temperatures up to 2700°C, thermal conductivities of about 13 W/m K and low dielectric constant [9][10][11][12]. It is a better semiconductor as compared to diamond and other commonly used semiconductors such as Si and GaN including, wide indirect band gap of 6.5 eV, can be doped as a shallow p-type semiconductor with Be and Mg and as an n-type with Si and Zn [13,14].…”