Metal–organic frameworks (MOFs) are promising materials with various applications, and machine learning
(ML) techniques can enable their design and understanding of structure–property relationships. In this
paper, we use machine learning (ML) to cluster the MOFs using two different approaches. For the first set
of clusters, we decompose the data using the textural properties and cluster the resulting components. We
separately cluster the MOF space with respect to their topology. The feature data from each of the clusters
were then fed into separate neural networks (NNs) for direct learning on an adsorption task (methane or
hydrogen). The resulting NNs were then used in transfer learning (TL) where only the last NN layer was
retrained. The results show significant differences in TL performance based on which cluster is chosen for
direct learning. We find TL performance depends on the Euclidean distance in the decomposed feature
space between the clusters involved in the direct and TL. Similar results were found when TL was
performed simultaneously across both types of clusters and adsorption tasks. We note that methane
adsorption was a better source task than hydrogen adsorption. Overall, the approach was able to identify
MOFs with the most transferable information, leading to valuable insights and a more comprehensive
understanding of the MOF landscape. This highlights the method's potential to generate a deeper
understanding of complex systems and provides an opportunity for its application in alternative datasets.
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Explaining the physics of transfer learning in data-driven turbulence modeling
Transfer learning (TL), which enables neural networks (NNs) to generalize out-of-distribution via targeted re-training, is becoming a powerful tool in scientific machine learning (ML) applications such as weather/climate prediction and turbulence modeling. Effective TL requires knowing (1) how to re-train NNs? and (2) what physics are learned during TL? Here, we present novel analyses and a framework addressing (1)–(2) for a broad range of multi-scale, nonlinear, dynamical systems. Our approach combines spectral (e.g. Fourier) analyses of such systems with spectral analyses of convolutional NNs, revealing physical connections between the systems and what the NN learns (a combination of low-, high-, band-pass filters and Gabor filters). Integrating these analyses, we introduce a general framework that identifies the best re-training procedure for a given problem based on physics and NN theory. As test case, we explain the physics of TL in subgrid-scale modeling of several setups of 2D turbulence. Furthermore, these analyses show that in these cases, the shallowest convolution layers are the best to re-train, which is consistent with our physics-guided framework but is against the common wisdom guiding TL in the ML literature. Our work provides a new avenue for optimal and explainable TL, and a step toward fully explainable NNs, for wide-ranging applications in science and engineering, such as climate change modeling.
more » « less- Award ID(s):
- 2005123
- NSF-PAR ID:
- 10472803
- Editor(s):
- Yortsos, Yannis
- Publisher / Repository:
- Oxford University
- Date Published:
- Journal Name:
- PNAS Nexus
- Volume:
- 2
- Issue:
- 3
- ISSN:
- 2752-6542
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1093/pnasnexus/pgad015
