Chia-Chun Lin
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Chia-Chun Lin has a total of 3 co-authors. Here are the top co-authors by number of shared publications:
- Chao-Cheng Kaun
Academia Sinica
1 shared publication - Chia-Wei Chang
Department of Materials Science and Engineering, National Cheng Kung University, Taiwan
1 shared publication - Yen-Hsun Su
Department of Materials Science and Engineering, National Cheng Kung University, No. 1, Daxue Road, East District, Tainan City 701, Taiwan
1 shared publication
Latest Publications
- Open Access
Stepwise Evolution of Photocatalytic Spinel-Structured (Co,Cr,Fe,Mn,Ni)3O4 High Entropy Oxides from First-Principles Calculations to Machine Learning
High entropy oxides (HEOx) are novel materials, which increase the potential application in the fields of energy and catalysis. However, a series of HEOx is too novel to evaluate the synthesis properties, including formation and fundamental properties. Combining first-principles calculations with machine learning (ML) techniques, we predict the lattice constants and formation energies of spinel-structured photocatalytic HEOx, (Co,Cr,Fe,Mn,Ni)3O4, for stoichiometric and non-stoichiometric structures. The effects of site occupation by different metal cations in the spinel structure are obtained through first-principles calculations and ML predictions. Our predicted results show that the lattice constants of these spinel-structured oxides are composition-dependent and that the formation energies of those oxides containing Cr atoms are low. The computing time and computing energy can be greatly economized through the tandem approach of first-principles calculations and ML.
Open AccessStepwise Evolution of Photocatalytic Spinel-Structured (Co,Cr,Fe,Mn,Ni)3O4 High Entropy Oxides from First-Principles Calculations to Machine Learning
High entropy oxides (HEOx) are novel materials, which increase the potential application in the fields of energy and catalysis. However, a series of HEOx is too novel to evaluate the synthesis properties, including formation and fundamental properties. Combining first-principles calculations with machine learning (ML) techniques, we predict the lattice constants and formation energies of spinel-structured photocatalytic HEOx, (Co,Cr,Fe,Mn,Ni)3O4, for stoichiometric and non-stoichiometric structures. The effects of site occupation by different metal cations in the spinel structure are obtained through first-principles calculations and ML predictions. Our predicted results show that the lattice constants of these spinel-structured oxides are composition-dependent and that the formation energies of those oxides containing Cr atoms are low. The computing time and computing energy can be greatly economized through the tandem approach of first-principles calculations and ML.