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Oct 15, 2021 -
Code for: Fast and Sample-Efficient Interatomic Neural Network Potentials for Molecules and Materials Based on Gaussian Moments
Python Source Code - 5.4 KB -
MD5: e031f8dc92eb579663a3121c9919b3ba
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Oct 5, 2021 - PN 6-6
Tkachev, Gleb, 2021, "Replication Data for: "S4: Self-Supervised learning of Spatiotemporal Similarity"", https://doi.org/10.18419/DARUS-2174, DaRUS, V1
We train a self-supervised siamese model that enables querying for similar behavior on spatiotemporal volumes. Here we provide the code and data needed to reproduce the representative figures of the paper. See the notes and the included readme file for details. |
Markdown Text - 6.5 KB -
MD5: 68f130a767c01781ac014ef8b22e15e0
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Python Source Code - 5.7 KB -
MD5: 01ec21f3e3f07f33187b224dca902de7
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Python Source Code - 1.1 KB -
MD5: 5c7f9cd6a0100ffcabad2f08c9dbc9f4
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ZIP Archive - 320.0 MB -
MD5: 4036d79b7568f32af4997ada8dce8887
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HTML - 25.2 KB -
MD5: db2af53609e7f330d7a7213a8ed648b8
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Cascading Style Sheet - 16.6 KB -
MD5: c2eb900af89a9e65412a15117d1bd637
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Unknown - 22.8 KB -
MD5: 05864fe0a2b820cdd805e34ef5f62dd5
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Python Source Code - 17.1 KB -
MD5: d498981bc6adbed8207ac2380d47eaf2
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