Graph Comparison of Molecular Crystals in Band Gap Prediction Using Neural Networks
In material informatics, the representation of the material structure is fundamentally essential to obtaining better prediction results, and graph representation has attracted much attention in recent years. Molecular crystals can be graphically represented in molecular and crystal representations,...
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| Published in | ACS omega Vol. 8; no. 42; pp. 39481 - 39489 |
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| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
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American Chemical Society
24.10.2023
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| Abstract | In material informatics, the representation of the material structure is fundamentally essential to obtaining better prediction results, and graph representation has attracted much attention in recent years. Molecular crystals can be graphically represented in molecular and crystal representations, but a comparison of which representation is more effective has not been examined. In this study, we compared the prediction accuracy between molecular and crystal graphs for band gap prediction. The results showed that the prediction accuracies using crystal graphs were better than those obtained using molecular graphs. While this result is not surprising, error analysis quantitatively evaluated that the error of the crystal graph was 0.4 times that of the molecular graph with moderate correlation. The novelty of this study lies in the comparison of molecular crystal representations and in the quantitative evaluation of the contribution of crystal structures to the band gap. |
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| AbstractList | In material informatics,
the representation of the material structure
is fundamentally essential to obtaining better prediction results,
and graph representation has attracted much attention in recent years.
Molecular crystals can be graphically represented in molecular and
crystal representations, but a comparison of which representation
is more effective has not been examined. In this study, we compared
the prediction accuracy between molecular and crystal graphs for band
gap prediction. The results showed that the prediction accuracies
using crystal graphs were better than those obtained using molecular
graphs. While this result is not surprising, error analysis quantitatively
evaluated that the error of the crystal graph was 0.4 times that of
the molecular graph with moderate correlation. The novelty of this
study lies in the comparison of molecular crystal representations
and in the quantitative evaluation of the contribution of crystal
structures to the band gap. In material informatics, the representation of the material structure is fundamentally essential to obtaining better prediction results, and graph representation has attracted much attention in recent years. Molecular crystals can be graphically represented in molecular and crystal representations, but a comparison of which representation is more effective has not been examined. In this study, we compared the prediction accuracy between molecular and crystal graphs for band gap prediction. The results showed that the prediction accuracies using crystal graphs were better than those obtained using molecular graphs. While this result is not surprising, error analysis quantitatively evaluated that the error of the crystal graph was 0.4 times that of the molecular graph with moderate correlation. The novelty of this study lies in the comparison of molecular crystal representations and in the quantitative evaluation of the contribution of crystal structures to the band gap. In material informatics, the representation of the material structure is fundamentally essential to obtaining better prediction results, and graph representation has attracted much attention in recent years. Molecular crystals can be graphically represented in molecular and crystal representations, but a comparison of which representation is more effective has not been examined. In this study, we compared the prediction accuracy between molecular and crystal graphs for band gap prediction. The results showed that the prediction accuracies using crystal graphs were better than those obtained using molecular graphs. While this result is not surprising, error analysis quantitatively evaluated that the error of the crystal graph was 0.4 times that of the molecular graph with moderate correlation. The novelty of this study lies in the comparison of molecular crystal representations and in the quantitative evaluation of the contribution of crystal structures to the band gap.In material informatics, the representation of the material structure is fundamentally essential to obtaining better prediction results, and graph representation has attracted much attention in recent years. Molecular crystals can be graphically represented in molecular and crystal representations, but a comparison of which representation is more effective has not been examined. In this study, we compared the prediction accuracy between molecular and crystal graphs for band gap prediction. The results showed that the prediction accuracies using crystal graphs were better than those obtained using molecular graphs. While this result is not surprising, error analysis quantitatively evaluated that the error of the crystal graph was 0.4 times that of the molecular graph with moderate correlation. The novelty of this study lies in the comparison of molecular crystal representations and in the quantitative evaluation of the contribution of crystal structures to the band gap. |
| Author | Taniguchi, Takuya Asahi, Toru Hosokawa, Mayuko |
| AuthorAffiliation | Center for Data Science Department of Advanced Science and Engineering, Graduate School of Advanced Science and Engineering Waseda University |
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| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/37901497$$D View this record in MEDLINE/PubMed |
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| Title | Graph Comparison of Molecular Crystals in Band Gap Prediction Using Neural Networks |
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