A KAN-based interpretable framework for prediction of global warming potential across chemical space
Accurate yet interpretable prediction of Global Warming Potential (GWP) is essential for the sustainable design of novel molecules, chemical processes and materials. This capability is valuable in the early-stage screening of compounds with potential relevance to carbon management and emerging CCUS...
Saved in:
| Main Authors: | Jaewook Lee, Xinyang Sun, Ethan Errington, Calum Drysdale, Miao Guo |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-09-01
|
| Series: | Carbon Capture Science & Technology |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772656825001174 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Kolmogorov–Arnold Networks for Interpretable Crop Yield Prediction Across the U.S. Corn Belt
by: Mustafa Serkan Isik, et al.
Published: (2025-07-01) -
SineKAN: Kolmogorov-Arnold Networks using sinusoidal activation functions
by: Eric Reinhardt, et al.
Published: (2025-01-01) -
A multivariate time series prediction model based on the KAN network
by: Yunji Long, et al.
Published: (2025-07-01) -
AKTMD: Attention-KAN-Based Neural Networks for Transportation Mode Detection
by: Rui Li, et al.
Published: (2025-01-01) -
Bearing fault diagnosis for variable operating conditions based on KAN convolution and dual branch fusion attention
by: Qibing Wang, et al.
Published: (2025-07-01)