Best practices in machine learning for chemistry - Nature Chemistry

The application of statistical machine learning techniques in chemistry has a long history1. Algorithmic innovation, improved data availability, and increases in computer power have led to an unprecedented growth in the field2,3. Extending the previous generation of high-throughput methods, and building on the many extensive and curated databases available, the ability to map between the chemical structure of molecules and materials and their physical properties has been widely demonstrated using supervised learning for both regression (for example, reaction rate) and classification (for example, reaction outcome) problems. Notably, molecular modelling has benefited from interatomic potentials based on Gaussian processes4 and artificial neural networks5 that can reproduce structural transformations at a fraction of the cost required by standard first-principles simulation techniques. The research literature itself has become a valuable resource for mining latent knowledge using natural language processing, as recently applied to extract synthesis recipes for inorganic crystals6.