Title:	Screening Extreme Conditions Materials Properties Through Coordinate Free Representations
DNr:	Berzelius-2024-326
Project Type:	LiU Berzelius
Principal Investigator:	Florian Trybel <florian.trybel@liu.se>
Affiliation:	Linköpings universitet
Duration:	2024-09-01 – 2025-03-01
Classification:	10304
Homepage:	https://liu.se/en/employee/flotr82
Keywords:

Abstract

A wide range of technologically relevant properties are accessible in functional materials under high pressure (P), but often vanish under decompression, either because the atomic arrangement or the underlying physical process is unfavourable at ambient conditions, which is particularly true for nitrides, carbides and carbo-nitrides. These classes of compounds are known to exhibit outstanding mechanical and electronic properties, relevant for hard coatings, solid-state lighting, photovoltaics as well as being green high-energy-density materials. Despite their outstanding potential, nitrogen-bearing compounds are still strikingly uncommon in the Inorganic Crystal Structure Database (ICSD) database, as nitrogen is only weakly reactive at ambient conditions due to the high stability of the triple bonded N2 molecule. At high P and under laser heating, however, nitrogen becomes significantly more reactive and forms stable -- often strongly covalent -- compounds with a variety of elements. These compounds show an unexpectedly rich and complex chemistry and long sought after poly-N species, e.g. rings with 5, 6, up to 18 atomic, some aromatic, helix like arrangements or layered structures with technologically relevant electronic properties, e.g., Dirac metals. Furthermore, together with my experimental collaborators, we were able to synthesise and characterise long sought-after polymeric C-N compounds at P > 100 GPa and recover them to ambient conditions [1]. Our calculations predict hardness values and electronic properties similar to diamond, but possibly with an even greater multi-functionality. The aim of this project is to investigate how coordinate free representations can be used to perform a machine-learning-based screening for technologically relevant mechanical and chemical properties of high-pressure synthesisable compounds. [1] Laniel, Trybel, et al. (2023). Synthesis of Ultra‐Incompressible and Recoverable Carbon Nitrides Featuring CN4 Tetrahedra. Advanced Materials, 2308030.