ArticleJournal of the American Chemical Society2023
MOFormer: Self-Supervised Transformer Model for Metal-Organic Framework Property Prediction.
Article in Journal of the American Chemical Society, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.
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Who cites it
33 citing papers in PubMed, 144 citations in OpenAlex.
- Multimodal Transformer for Sample-Aware Prediction of Metal-Organic Framework Properties.ACS central science · 2026Article
- GFN1-xTB-Assisted Machine Learning for Electronic Structure Screening of Metal-Organic Frameworks.Journal of chemical theory and computation · 2026Article
- Interaction topology theory deciphers multiscale codes of MOF-like materials.Science advances · 2026Article
- A Visualization Analysis of Machine Learning Applications in Gas Adsorption Using Nanoporous Materials.Nanomaterials (Basel, Switzerland) · 2026Article
- PoroNet: An Intrinsically Interpretable Pore Graph Neural Network for Resolving Pore-Level Adsorption in Metal-Organic Frameworks.Journal of chemical theory and computation · 2026Article
- MOFSynth-ADV: An Open-Source Engine for Synthesizability Evaluation of Metal-Organic Frameworks.Journal of chemical information and modeling · 2026Article
- Polymer-Agent: Large Language Model Agent for Polymer Design.Journal of chemical information and modeling · 2026Article
- RetNeXt: A Pretrained Model for Transfer Learning Across the MOF Adsorption Space.Journal of chemical information and modeling · 2026Article
- A data-efficient foundation model for porous materials based on expert-guided supervised learning.Nature communications · 2026Article
- Deep Learning Framework for Atomic-Level Design and Presynthesis Prediction of Coinage-Metal Nanoclusters.ACS central science · 2026Article
- 32 examples of LLM applications in materials science and chemistry: towards automation, assistants, agents, and accelerated scientific discovery.Machine learning: science and technology · 2025Article
- MOFGPT: Generative Design of Metal-Organic Frameworks using Language Models.Journal of chemical information and modeling · 2025Article
- A universal foundation model for transfer learning in molecular crystals.Chemical science · 2025Article
- Artificial Intelligence Paradigms for Next-Generation Metal-Organic Framework Research.Journal of the American Chemical Society · 2025Review
- Connecting metal-organic framework synthesis to applications using multimodal machine learning.Nature communications · 2025Article
- Quantifying Gas Adsorption Variability and Optimal Si/Al Ratio for Rational Design of Aluminum-Substituted Zeolite Frameworks.Langmuir : the ACS journal of surfaces and colloids · 2025Article
- Inverse design of metal-organic frameworks using deep dreaming approaches.Nature communications · 2025Article
- Category-specific topological learning of metal-organic frameworks.Journal of materials chemistry. A · 2025Article
- Understanding the Electrochemical MOF Sensors in Detecting Cancer with Special Emphasis on Breast Carcinoma Biomarkers.Topics in current chemistry (Cham) · 2025Review
- PolyCL: contrastive learning for polymer representation learningDigital discovery · 2025Article
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Metal-organic frameworks (MOFs) are materials with a high degree of porosity that can be used for many applications. However, the chemical space of MOFs is enormous due to the large variety of possible combinations of building blocks and topology. Discovering the optimal MOFs for specific applications requires an efficient and accurate search over countless potential candidates. Previous high-throughput screening methods using computational simulations like DFT can be time-consuming. Such methods also require the 3D atomic structures of MOFs, which adds one extra step when evaluating hypothetical MOFs. In this work, we propose a structure-agnostic deep learning method based on the Transformer model, named as MOFormer, for property predictions of MOFs. MOFormer takes a text string representation of MOF (MOFid) as input, thus circumventing the need of obtaining the 3D structure of a hypothetical MOF and accelerating the screening process. By comparing to other descriptors such as Stoichiometric-120 and revised autocorrelations, we demonstrate that MOFormer can achieve state-of-the-art structure-agnostic prediction accuracy on all benchmarks. Furthermore, we introduce a self-supervised learning framework that pretrains the MOFormer via maximizing the cross-correlation between its structure-agnostic representations and structure-based representations of the crystal graph convolutional neural network (CGCNN) on >400k publicly available MOF data. Benchmarks show that pretraining improves the prediction accuracy of both models on various downstream prediction tasks. Furthermore, we revealed that MOFormer can be more data-efficient on quantum-chemical property prediction than structure-based CGCNN when training data is limited. Overall, MOFormer provides a novel perspective on efficient MOF property prediction using deep learning.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.