jeremy1189 Goto Github PK

github-slideshow

A robot powered training repository :robot:

influence-of-short-range-order-in-nicrco

This project ains to explore the influence of short range order in NiCoCr

interstitial-diffusion

The differential evolution for obtaining the optimization interstiial diffusion coefficient in concentrated solute alloy(CSAs)

jeremy1189

Config files for my GitHub profile.

megnet

Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals

ml_sluggish_diffusion

Single-phase concentrated solid solution alloys (CSAs), including medium- and high-entropy alloys (HEAs), show outstanding functional and structural properties, which are thought to be related to their sluggish diffusion properties. Despite decades of efforts, the origin of sluggish diffusion and even whether it exists at all, are still under intense debate. Generally, sluggish diffusion is understood as a result of the rough potential energy landscape (PEL) in CSAs due to the intrinsic chemically disordered states. Nonetheless, how the rugged PEL affects diffusion properties remains elusive due to the complications induced by the enormous atomic environments experienced by diffusing atoms. Here, we uncover the links between the PEL and self-diffusion in CSAs by combining machine learning (ML) and kinetic Monte Carlo (kMC).

nicocr--short-range-order

This code show how to combine the Machine learning with the kinetic Monte Carlo(kMC) to performa a diffusion simulation in NiCoCr. During the diffusion, the Warren-Cowley short-range order , tracer diffusion coefficients, correlation factor and so on are calculated.

nicocr_sro

revealing-the-crucial-role-of-rough-energy-landscape-on-self-diffusion-in-high-entropy-alloys

Most of the outstanding functional and structural performance in high-entropy alloys (HEAs) relates to their sluggish diffusion properties under the rough potential energy landscape (PEL) induced by intrinsic chemical disorder. Due to the highly rugged and multi-dimensional nature of PEL, it is challenging to describe how the diffusion process is controlled by the PEL in HEAs. Here we develop machine learning (ML) models to accurately represent the local atomic environment dependence of PEL in HEAs. By combining the ML model with the kinetic Monte Carlo (kMC) method, we reveal that self-diffusion in HEAs is predominantly governed by the PEL roughness, as characterized by the elemental-specific site energies and migration barriers. Comparisons with previously-proposed simplified models for self-diffusion in HEAs elucidate that the species-averaged model may be a suitable alternative method to rapidly assess diffusion properties, though the correlation effects may be underestimated. Aided by theoretical analysis, we show that the atomic concentrations of fast-diffusing elements and the differences in the averaged migration barriers for different species are the dominant factors influencing sluggish diffusion in HEAs.