Given a concentration, the determination of atomic configuration of a compound is very challenging, although it is the base and prerequisite for the investigation of the properties. Taken the Mg-Bi-Sb alloy system as an example, we have investigated the crystal structure of the Mg3Bi2-xSbx(0 < x < 2) using the revised LAsou method with an active learning strategy. We have explicitly examined the atomistic configurations with three unit-cell sizes (10, 40, and 90 atoms). Referring to the conventional method, our method reduces the computational demand by several order of magnitude. Structural analysis including bond-order parameter, disorder parameter, short-range order parameter, and radial distribution function, revealed that the 90-atom u...
The liquid Mg-Bi system exhibits strong compound formation at the 'octet' composition (Mg3Bi2) We pr...
The prediction of energetically stable crystal structures formed by a given chemical composition is ...
peer reviewedMachine Learning (ML) techniques are revolutionizing the way to perform efficient mate...
Given a concentration, the determination of atomic configuration of a compound is very challenging, ...
Given a concentration, the determination of atomic configuration of a compound is very challenging, ...
Machine Learning (ML) techniques are revolutionizing the way to perform efficient materials modeling...
This thesis develops a machine learning framework for predicting crystal structure and applies it to...
The computational design of new and interesting inorganic materials is still an ongoing challenge. ...
We present a combination of machine learning and high throughput calculations to predict the points ...
We present a combination of machine learning and high throughput calculations to predict the points ...
One of the great challenges of modern science is to faithfully model, and understand, matter at a wi...
The liquid Mg-Bi system exhibits strong compound formation at the 'octet' composition (Mg3Bi2) We pr...
The liquid Mg-Bi system exhibits strong compound formation at the 'octet' composition (Mg3Bi2) We pr...
The liquid Mg-Bi system exhibits strong compound formation at the 'octet' composition (Mg3Bi2) We pr...
The liquid Mg-Bi system exhibits strong compound formation at the 'octet' composition (Mg3Bi2) We pr...
The liquid Mg-Bi system exhibits strong compound formation at the 'octet' composition (Mg3Bi2) We pr...
The prediction of energetically stable crystal structures formed by a given chemical composition is ...
peer reviewedMachine Learning (ML) techniques are revolutionizing the way to perform efficient mate...
Given a concentration, the determination of atomic configuration of a compound is very challenging, ...
Given a concentration, the determination of atomic configuration of a compound is very challenging, ...
Machine Learning (ML) techniques are revolutionizing the way to perform efficient materials modeling...
This thesis develops a machine learning framework for predicting crystal structure and applies it to...
The computational design of new and interesting inorganic materials is still an ongoing challenge. ...
We present a combination of machine learning and high throughput calculations to predict the points ...
We present a combination of machine learning and high throughput calculations to predict the points ...
One of the great challenges of modern science is to faithfully model, and understand, matter at a wi...
The liquid Mg-Bi system exhibits strong compound formation at the 'octet' composition (Mg3Bi2) We pr...
The liquid Mg-Bi system exhibits strong compound formation at the 'octet' composition (Mg3Bi2) We pr...
The liquid Mg-Bi system exhibits strong compound formation at the 'octet' composition (Mg3Bi2) We pr...
The liquid Mg-Bi system exhibits strong compound formation at the 'octet' composition (Mg3Bi2) We pr...
The liquid Mg-Bi system exhibits strong compound formation at the 'octet' composition (Mg3Bi2) We pr...
The prediction of energetically stable crystal structures formed by a given chemical composition is ...
peer reviewedMachine Learning (ML) techniques are revolutionizing the way to perform efficient mate...