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2010.00532
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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
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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
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
Frontiers 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
Nicolas Swenson
Aditi S. Krishnapriyan
A. Buluç
Dmitriy Morozov
Katherine Yelick
179
20
0
30 Oct 2020
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