  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. It
automatically encapsulates geometric and chemical information directly from the
material system. We demonstrate our approach on multiple nanoporous
metal-organic framework datasets by predicting methane and carbon dioxide
adsorption across different conditions. Our results show considerable
improvement in both accuracy and transferability across targets compared to
models constructed from the commonly-used, manually-curated features,
consistently achieving an average 25-30% decrease in
root-mean-squared-deviation and an average increase of 40-50% in R2 scores. A
key advantage of our approach is interpretability: Our model identifies the
pores that correlate best to adsorption at different pressures, which
contributes to understanding atomic-level structure--property relationships for
materials design.
