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Found 5 papers, 4 papers with code
On the Quantification of Image Reconstruction Uncertainty without Training Data
This, in turn, facilitates a probabilistic interpretation of observational data for decision-making.
GDL-DS: A Benchmark for Geometric Deep Learning under Distribution Shifts
Geometric deep learning (GDL) has gained significant attention in various scientific fields, chiefly for its proficiency in modeling data with intricate geometric structures.
Accelerating Inverse Learning via Intelligent Localization with Exploratory Sampling
In the scope of "AI for Science", solving inverse problems is a longstanding challenge in materials and drug discovery, where the goal is to determine the hidden structures given a set of desirable properties.
Atomic structure generation from reconstructing structural fingerprints
These methods would help identify or, in the case of generative models, even create novel crystal structures of materials with a set of specified functional properties to then be synthesized or isolated in the laboratory.
Inverse design of two-dimensional materials with invertible neural networks
The ability to readily design novel materials with chosen functional properties on-demand represents a next frontier in materials discovery.
