Training datasets for artificial intelligence based chemistry models typically rely on experimental data from optimised synthetic conditions only, leading to an inherited bias in the model predictions. Here, the authors develop an artificial intelligence model based on a variational autoencoder to synthetically generate continuous datasets and generate new chemical reactions in a less biased way, by sampling the entirety of the solution space.
- Robert Tempke
- Terence Musho
