TLDR
Material scientists often face the challenge of figuring out how to effectively search the vast chemical design space to locate the materials with their desired properties. The use of GANs can be leveraged to generate hypothetical materials not existing in the training dataset, reaching a novelty of 92.53% when generating 2 million samples. The implications of general adversarial networks extend far beyond the applications in material science, but I have admit that this is one of the use cases I have really would have thought of applying AI to.via the TL;DR App
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Written by jdbohrman | Cloud-native engineer. Writer. Eventual pile of dust. As above, so below.
