Machine learning with persistent homology and chemical word embeddings improves prediction accuracy and interpretability in metal-organic frameworks

Understand

Machine learning has emerged as a powerful approach in materials discovery.

• Its major challenge is selecting features that create interpretable representations of materials, useful across multiple prediction tasks.
• We introduce an end-to-end machine learning model that automatically generates descriptors that capture a complex representation of a material's structure and chemistry.
• This approach builds on computational topology techniques (namely, persistent homology) and word embeddings from natural language processing.