Machine learning reveals orbital interaction in materials
We propose a novel representation of materials named an ‘orbital-field matrix (OFM)’, which is based on the distribution of valence shell electrons. We demonstrate that this new representation can be highly useful in mining material data. Experimental investigation shows that the formation energies...
Main Authors: | Tien Lam Pham, Hiori Kino, Kiyoyuki Terakura, Takashi Miyake, Koji Tsuda, Ichigaku Takigawa, Hieu Chi Dam |
---|---|
Format: | Article |
Language: | English |
Published: |
Taylor & Francis Group
2017-12-01
|
Series: | Science and Technology of Advanced Materials |
Subjects: | |
Online Access: | http://dx.doi.org/10.1080/14686996.2017.1378060 |
Similar Items
-
Explainable machine learning for materials discovery: predicting the potentially formable Nd–Fe–B crystal structures and extracting the structure–stability relationship
by: Tien-Lam Pham, et al.
Published: (2020-11-01) -
Committee machine that votes for similarity between materials
by: Duong-Nguyen Nguyen, et al.
Published: (2018-11-01) -
Convolutional Neural Networks for Crystal Material Property Prediction Using Hybrid Orbital-Field Matrix and Magpie Descriptors
by: Zhuo Cao, et al.
Published: (2019-04-01) -
A general representation scheme for crystalline solids based on Voronoi-tessellation real feature values and atomic property data
by: Randy Jalem, et al.
Published: (2018-12-01) -
Corrosion rate prediction and influencing factors evaluation of low-alloy steels in marine atmosphere using machine learning approach
by: Luchun Yan, et al.
Published: (2020-01-01)