# MOFSimplify: Machine Learning Models with Extracted Stability Data of Three Thousand Metal-Organic Frameworks

arXiv: https://arxiv.org/abs/2109.08098 · HTML: https://mlsanity.com/paper/2109.08098/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 81 references that are in the map.
- First posted on arXiv 16 Sep 2021.

## Builds on (most cited first, 2 of 2)

- [Inorganic Materials Synthesis Planning with Literature-Trained Neural Networks](https://mlsanity.com/paper/1901.00032/) (2018)
- [Using Machine Learning and Data Mining to Leverage Community Knowledge for the Engineering of Stable Metal-Organic Frameworks](https://mlsanity.com/paper/2106.13327/) (2021)

## Built on by (most cited first, 11 of 11)

- [MOFDiff: Coarse-grained Diffusion for Metal-Organic Framework Design](https://mlsanity.com/paper/2310.10732/) (2023)
- [Audacity of huge: overcoming challenges of data scarcity and data quality for machine learning in computational materials discovery](https://mlsanity.com/paper/2111.01905/) (2021)
- [Rapid Design of Top-Performing Metal-Organic Frameworks with Qualitative Representations of Building Blocks](https://mlsanity.com/paper/2302.09184/) (2023)
- [A Database of Ultrastable MOFs Reassembled from Stable Fragments with Machine Learning Models](https://mlsanity.com/paper/2210.14191/) (2022)
- [Automated Review Generation Method Based on Large Language Models](https://mlsanity.com/paper/2407.20906/) (2024)
- [Image and Data Mining in Reticular Chemistry Using GPT-4V](https://mlsanity.com/paper/2312.05468/) (2023)
- [Building-Block Aware Generative Modeling for 3D Crystals of Metal Organic Frameworks](https://mlsanity.com/paper/2505.08531/) (2025)
- [Building Open Knowledge Graph for Metal-Organic Frameworks (MOF-KG): Challenges and Case Studies](https://mlsanity.com/paper/2207.04502/) (2022)
- [CarbNN: A Novel Active Transfer Learning Neural Network To Build De Novo Metal Organic Frameworks (MOFs) for Carbon Capture](https://mlsanity.com/paper/2311.16158/) (2023)
- [Reducing cross-sample prediction churn in scientific machine learning](https://mlsanity.com/paper/2605.13826/) (2026)
- [Inverse Design of Inorganic Compounds with Generative AI](https://mlsanity.com/paper/2604.11827/) (2026)

## Abstract (arXiv)

We report a workflow and the output of a natural language processing (NLP)-based procedure to mine the extant metal-organic framework (MOF) literature describing structurally characterized MOFs and their solvent removal and thermal stabilities. We obtain over 2,000 solvent removal stability measures from text mining and 3,000 thermal decomposition temperatures from thermogravimetric analysis data. We assess the validity of our NLP methods and the accuracy of our extracted data by comparing to a hand-labeled subset. Machine learning (ML, i.e. artificial neural network) models trained on this data using graph- and pore-geometry-based representations enable prediction of stability on new MOFs with quantified uncertainty. Our web interface, MOFSimplify, provides users access to our curated data and enables them to harness that data for predictions on new MOFs. MOFSimplify also encourages community feedback on existing data and on ML model predictions for community-based active learning for improved MOF stability models.