Topological representations of crystalline compounds for the machine-learning prediction of materials properties.
- DOI
- 10.1038/s41524-021-00493-w
- Published
- 2021
- Container
- npj computational materials
- Publisher
- Not recorded
- Open access
- yes
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Cite this work
BibTeX
@article{allodium:10.1038/s41524-021-00493-w,
title = {Topological representations of crystalline compounds for the machine-learning prediction of materials properties.},
author = {Jiang Y and Chen D and Chen X and Li T and Wei GW and Pan F},
year = {2021},
journal = {npj computational materials},
doi = {10.1038/s41524-021-00493-w},
url = {https://doi.org/10.1038/s41524-021-00493-w}
}RIS
TY - JOUR TI - Topological representations of crystalline compounds for the machine-learning prediction of materials properties. AU - Jiang Y AU - Chen D AU - Chen X AU - Li T AU - Wei GW AU - Pan F PY - 2021 JO - npj computational materials DO - 10.1038/s41524-021-00493-w UR - https://doi.org/10.1038/s41524-021-00493-w ER -
APA
Y, J., D, C., X, C., T, L., GW, W., & F, P. (2021). Topological representations of crystalline compounds for the machine-learning prediction of materials properties.. npj computational materials. https://doi.org/10.1038/s41524-021-00493-w
Source records
- pubmed · retrieved 2026-09-25T22:27:08.167Z