LAUSR

Abstract Hard and superhard materials are essential for a myriad of scientific, biomedical, and industrial applications. Their ability to resist indentation stems from the relationship between the crystal structure, chemical composition, and microstructure. The complexity of this interdependence has limited researchers to often conduct comprehensive experimental investigations, with occasional support by computation. One of the main difficulties is that hardness is influenced by many factors, which requires many comprehensive calculations to account for multiple length scales ranging from atomic interactions (intrinsic hardness) to long length scales encompassing microstructure (extrinsic hardness). Nevertheless, improvements in computational methodologies and access to high-performance computing clusters have provided essential insight on the mechanisms of deformation, as well as pinpointed some crystal chemical traits that generate high hardness. This contribution discusses notable computational developments in the field spanning multiple length scales, including modeling the process of indentation through molecular dynamics, identifying computational theories of hardness through density functional theory, and screening for optimal materials through machine learning. Reviewing these research accomplishments not only highlights some important projects, but it also provides indications of potential research directions and opportunities to advance the development of hard and superhard materials through computation.