AKA From Deep Learning to Deep Reasoning
DRNets can solve Sudoku, speed scientific discovery
Sep 2021, phys.org
You can teach a machine to recognize a dog by showing it 1,000 pictures of dogs, Gomes said, but scientific discovery is not like that."You are not going to have lots and lots of labeled data," she said. "And in general, the examples you have are not exactly what you are looking for, but then you reason about what you know scientifically about the domain, and you can infer new knowledge."Key to DRNets is the idea of an "interpretable latent space." Basically, it gives DRNets the ability to reason about the constraints of the domain—in this case materials science—from input data.They started with Sudoku -- de-mixing overlapping handwritten Sudoku puzzles—grids. The computer had to separate the puzzles into two solved Sudokus, without any training data, which it was able to achieve with close to 100% accuracy.The researchers then put DRNets to work on a real-world problem: automating crystal-structure phase mapping of solar-fuels materials, using X-ray diffraction (XRD) patterns. Crystal-structure phase mapping involves separating the source XRD signals of the desired crystal structures from "noisy" mixtures of XRD patterns, a task for which labeled training data are typically not available. ... DRNets was able to identify and separate a total of 13 crystal phases (single-phase materials) in 19 unique mixtures of the single-phase materials. ... DRNets' findings, verified using manual analysis, enable the discovery of complex mixtures of crystalline materials that convert solar energy into storable solar chemical fuels.
via Cornell University: Di Chen et al, Automating crystal-structure phase mapping by combining deep learning with constraint reasoning, Nature Machine Intelligence (2021). DOI: 10.1038/s42256-021-00384-1
Image credit: De-Mixed Sudoko, Cornell University, 2021
No comments:
Post a Comment