# Using Machine Learning and Data Mining to Leverage Community Knowledge for the Engineering of Stable Metal-Organic Frameworks

arXiv: https://arxiv.org/abs/2106.13327 · HTML: https://mlsanity.com/paper/2106.13327/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 86 references that are in the map.
- First posted on arXiv 24 Jun 2021.

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

- [Stanza: A Python Natural Language Processing Toolkit for Many Human Languages](https://mlsanity.com/paper/2003.07082/) (2020)
- [Inorganic Materials Synthesis Planning with Literature-Trained Neural Networks](https://mlsanity.com/paper/1901.00032/) (2018)

## Built on by (most cited first, 20 of 20)

- [ChatGPT Chemistry Assistant for Text Mining and Prediction of MOF Synthesis](https://mlsanity.com/paper/2306.11296/) (2023)
- [MOFormer: Self-Supervised Transformer model for Metal-Organic Framework Property Prediction](https://mlsanity.com/paper/2210.14188/) (2022)
- [The Open DAC 2023 Dataset and Challenges for Sorbent Discovery in Direct Air Capture](https://mlsanity.com/paper/2311.00341/) (2023)
- [Agent-based Learning of Materials Datasets from Scientific Literature](https://mlsanity.com/paper/2312.11690/) (2023)
- [MOFSimplify: Machine Learning Models with Extracted Stability Data of Three Thousand Metal-Organic Frameworks](https://mlsanity.com/paper/2109.08098/) (2021)
- [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)
- [Machine Learning-Based Prediction of Metal-Organic Framework Materials: A Comparative Analysis of Multiple Models](https://mlsanity.com/paper/2507.04493/) (2025)
- [A Database of Ultrastable MOFs Reassembled from Stable Fragments with Machine Learning Models](https://mlsanity.com/paper/2210.14191/) (2022)
- [Image and Data Mining in Reticular Chemistry Using GPT-4V](https://mlsanity.com/paper/2312.05468/) (2023)
- [The Rise of Generative AI for Metal-Organic Framework Design and Synthesis](https://mlsanity.com/paper/2508.13197/) (2025)
- [MatGD: Materials Graph Digitizer](https://mlsanity.com/paper/2311.12806/) (2023)
- [Clever Materials: When Models Identify Good Materials for the Wrong Reasons](https://mlsanity.com/paper/2602.17730/) (2026)
- [Machine Learning Based Prediction of Proton Conductivity in Metal-Organic Frameworks](https://mlsanity.com/paper/2407.09514/) (2024)
- [EGMOF: Efficient Generation of Metal-Organic Frameworks Using a Hybrid Diffusion-Transformer Architecture](https://mlsanity.com/paper/2511.03122/) (2025)
- [Deciphering Cryptic Behavior in Bimetallic Transition Metal Complexes with Machine Learning](https://mlsanity.com/paper/2107.14280/) (2021)
- [Low-cost machine learning approach to the prediction of transition metal phosphor excited state properties](https://mlsanity.com/paper/2209.08595/) (2022)
- [Interaction Topological Transformer for Multiscale Learning in Porous Materials](https://mlsanity.com/paper/2509.18573/) (2025)
- [Inverse Design of Inorganic Compounds with Generative AI](https://mlsanity.com/paper/2604.11827/) (2026)
- [Multimodal Transformer for Sample-Aware Prediction of Metal-Organic Framework Properties](https://mlsanity.com/paper/2604.19383/) (2026)
- [Sustainable Metal-Organic Framework Water Harvesters in the Artificial Intelligence Era](https://mlsanity.com/paper/2605.29179/) (2026)

## Abstract (arXiv)

Although the tailored metal active sites and porous architectures of MOFs hold great promise for engineering challenges ranging from gas separations to catalysis, a lack of understanding of how to improve their stability limits their use in practice. To overcome this limitation, we extract thousands of published reports of the key aspects of MOF stability necessary for their practical application: the ability to withstand high temperatures without degrading and the capacity to be activated by removal of solvent molecules. From nearly 4,000 manuscripts, we use natural language processing and automated image analysis to obtain over 2,000 solvent-removal stability measures and 3,000 thermal degradation temperatures. We analyze the relationships between stability properties and the chemical and geometric structures in this set to identify limits of prior heuristics derived from smaller sets of MOFs. By training predictive machine learning (ML, i.e., Gaussian process and artificial neural network) models to encode the structure-property relationships with graph- and pore-structure-based representations, we are able to make predictions of stability orders of magnitude faster than conventional physics-based modeling or experiment. Interpretation of important features in ML models provides insights that we use to identify strategies to engineer increased stability into typically unstable 3d-containing MOFs that are frequently targeted for catalytic applications. We expect our approach to accelerate the time to discovery of stable, practical MOF materials for a wide range of applications.