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Machine learning with persistent homology and chemical word embeddings
  improves prediction accuracy and interpretability in metal-organic frameworks
v1v2 (latest)

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

1 October 2020
Aditi S. Krishnapriyan
Joseph H. Montoya
Maciej Haranczyk
J. Hummelshøj
Dmitriy Morozov
    AI4CE
ArXiv (abs)PDFHTML

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

3 / 3 papers shown
Learning from learning machines: a new generation of AI technology to
  meet the needs of science
Learning from learning machines: a new generation of AI technology to meet the needs of science
L. Pion-Tonachini
K. Bouchard
Héctor García Martín
S. Peisert
W. B. Holtz
...
Rick L. Stevens
Mark Anderson
Ken Kreutz-Delgado
Michael W. Mahoney
James B. Brown
221
9
0
27 Nov 2021
Topology Applied to Machine Learning: From Global to Local
Topology Applied to Machine Learning: From Global to LocalFrontiers in Artificial Intelligence (Front. Artif. Intell.), 2021
Henry Adams
Michael Moy
AI4CE
231
24
0
10 Mar 2021
PersGNN: Applying Topological Data Analysis and Geometric Deep Learning
  to Structure-Based Protein Function Prediction
PersGNN: Applying Topological Data Analysis and Geometric Deep Learning to Structure-Based Protein Function Prediction
Nicolas Swenson
Aditi S. Krishnapriyan
A. Buluç
Dmitriy Morozov
Katherine Yelick
179
20
0
30 Oct 2020
1
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