Ultrahigh hardness of carbon steel surface realized by novel solid carburizing with rapid diffusion of carbon nanostructures

In this study, a novel rapid solid carburizing process with a large diffusion depth using nano-diamonds（NDs） was conducted for low carbon steel. Changes of annealed NDs were obtained by Raman spectroscopy and transmission electron microscopy（TEM）, and the results suggested that the NDs experience a stripping process before a special solid-reaction with surface iron atoms from steel substrate. Onionlike carbon（OLC） derived from the annealed NDs provided broken graphitic ribbons as carbon sources that accelerated the rate of adsorption and diffusion. Examination of the surface layer at equilibrium using TEM and X-ray photoelectron spectroscopy（XPS） also revealed the special state of carbon, and an ultrafine mixed phase microstructure was obtained by rapid solid-phase transformation. As a result, a surface hardened layer with ultrahigh hardness and a smooth transition region were realized. We believe that these kinds of diamond or graphitic structures with high activity states have an important influence not only on adsorption and diffusion but also on this special solid-phase transformation.

Screening outstanding mechanical properties and low lattice thermal conductivity using global attention graph neural network

Mechanical and thermal properties of materials are extremely important for various engineering and scientific fields such as energy conversion and energy storage.However,the characterization of these properties via high throughput screening at the quantum level,although highly accurate,is inefficient and very time-and resource-consuming.In contrast,prediction at the classical level is highly efficient but less accurate.We deploy scalable global attention graph neural network for accurate prediction of mechanical properties which bridge the gap between the accuracy at the quantum level and efficiency at the classical level.Using 10,158 elastic constants as training data,we trained the models on 5 mechanical properties,namely bulk modulus,shear modulus,Young’s modulus,Poisson’s ratio,and hardness.With the trained model,we predicted 775,947 data in search of materials with ultrahigh hardness.We further verify the recommended ultrahigh hardness materials by high precision first principles calculations,and we finally identify 20 structures with extreme hardness close to diamond,the hardest material in nature.Among those,two super hard materials are completely new and have not been reported in literature so far.We further recommend potential materials from bulk modulus prediction to search low lattice thermal conductivity,and we verify the thermal conductivity of 338 structures with first principles.Our results demonstrate that one can find materials with extreme mechanical properties recommended by graph neural network and low thermal conductivity material from bulk modulus prediction with minimal first principles calculations of the structures(only 0.04%)in the large-scale materials pool.