Machine learning with persistent homology and chemical word embeddings improves prediction accuracy and interpretability in metal-organic frameworks.

Krishnapriyan AS, Montoya J, Haranczyk M, Hummelshøj J, Morozov D.

Open source

DOI
10.1038/s41598-021-88027-8
Published
2021-04-26
Container
Sci Rep
Publisher
Not recorded
Open access
yes

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BibTeX

@article{allodium:10.1038/s41598-021-88027-8,
  title = {Machine learning with persistent homology and chemical word embeddings improves prediction accuracy and interpretability in metal-organic frameworks.},
  author = {Krishnapriyan AS and  Montoya J and  Haranczyk M and  Hummelshøj J and  Morozov D.},
  year = {2021},
  journal = {Sci Rep},
  doi = {10.1038/s41598-021-88027-8},
  url = {https://doi.org/10.1038/s41598-021-88027-8}
}

RIS

TY  - JOUR
TI  - Machine learning with persistent homology and chemical word embeddings improves prediction accuracy and interpretability in metal-organic frameworks.
AU  - Krishnapriyan AS
AU  -  Montoya J
AU  -  Haranczyk M
AU  -  Hummelshøj J
AU  -  Morozov D.
PY  - 2021
JO  - Sci Rep
DO  - 10.1038/s41598-021-88027-8
UR  - https://doi.org/10.1038/s41598-021-88027-8
ER  -

APA

AS, K., J, M., M, H., J, H., & D., M. (2021). Machine learning with persistent homology and chemical word embeddings improves prediction accuracy and interpretability in metal-organic frameworks.. Sci Rep. https://doi.org/10.1038/s41598-021-88027-8

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