341 citations · 484 across the 4 of their papers we have counts for
5 papers
Accurate predictive model of band gap with selected important features based on explainable machine learning
Joohwi Lee, Kaito Miyamoto
In the rapidly advancing field of materials informatics, nonlinear machine learning models have demonstrated exceptional predictive capabilities for material properties. However, t…
Creation of crystal structure reproducing X-ray diffraction pattern without using database
Joohwi Lee, Junpei Oba, Nobuko Ohba +1
When a sample's X-ray diffraction pattern (XRD) is measured, the corresponding crystal structure is usually determined by searching for similar XRD patterns in the database. Howeve…
Transfer learning for materials informatics using crystal graph convolutional neural network
Joohwi Lee, Ryoji Asahi
For successful applications of machine learning in materials informatics, it is necessary to overcome the inaccuracy of predictions ascribed to insufficient amount of data. In this…
First-principles high-throughput screening of shape-memory alloys based on energetic, dynamical, and structural properties
Joohwi Lee, Yuji Ikeda, Isao Tanaka
First-principles-based materials screening is systematically performed to discover new combinations of chemical elements possibly making shape-memory alloys (SMAs). The B2, D03, an…
Prediction model of band-gap for AX binary compounds by combination of density functional theory calculations and machine learning techniques
Joohwi Lee, Atsuto Seko, Kazuki Shitara +1
Machine learning techniques are applied to make prediction models of the G0W0 band-gaps for 156 AX binary compounds using Kohn-Sham band-gaps and other fundamental information of c…