Machine learning with persistent homology and chemical word embeddings improves prediction accuracy and interpretability in metal-organic frameworks
- Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Toyota Research Inst., Los Altos, CA (United States)
- Toyota Research Inst., Los Altos, CA (United States)
- IMDEA Materials Inst., Madrid (Spain)
- Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
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.
- Research Organization:
- Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
- Sponsoring Organization:
- USDOE Office of Science (SC), Basic Energy Sciences (BES). Scientific User Facilities Division
- Grant/Contract Number:
- AC02-05CH11231
- OSTI ID:
- 1797749
- Journal Information:
- Scientific Reports, Vol. 11, Issue 1; ISSN 2045-2322
- Publisher:
- Nature Publishing GroupCopyright Statement
- Country of Publication:
- United States
- Language:
- English
Similar Records
Unsupervised word embeddings capture latent knowledge from materials science literature
Energy-based descriptors to rapidly predict hydrogen storage in metal–organic frameworks