# A generative artificial intelligence framework based on a molecular diffusion model for the design of metal-organic frameworks for carbon capture

arXiv: https://arxiv.org/abs/2306.08695 · HTML: https://mlsanity.com/paper/2306.08695/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 74 references that are in the map.
- First posted on arXiv 14 Jun 2023.

## Builds on (most cited first, 13 of 13)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [E(n) Equivariant Graph Neural Networks](https://mlsanity.com/paper/2102.09844/) (2021)
- [Equivariant Diffusion for Molecule Generation in 3D](https://mlsanity.com/paper/2203.17003/) (2022)
- [DiGress: Discrete Denoising diffusion for graph generation](https://mlsanity.com/paper/2209.14734/) (2022)
- [GeoDiff: a Geometric Diffusion Model for Molecular Conformation Generation](https://mlsanity.com/paper/2203.02923/) (2022)
- [DiffDock: Diffusion Steps, Twists, and Turns for Molecular Docking](https://mlsanity.com/paper/2210.01776/) (2022)
- [Ensemble deep learning: A review](https://mlsanity.com/paper/2104.02395/) (2021)
- [Molecular Sets (MOSES): A Benchmarking Platform for Molecular Generation Models](https://mlsanity.com/paper/1811.12823/) (2018)
- [Deep Generative Modelling: A Comparative Review of VAEs, GANs, Normalizing Flows, Energy-Based and Autoregressive Models](https://mlsanity.com/paper/2103.04922/) (2021)
- [Structure-based Drug Design with Equivariant Diffusion Models](https://mlsanity.com/paper/2210.13695/) (2022)
- [Equivariant 3D-Conditional Diffusion Models for Molecular Linker Design](https://mlsanity.com/paper/2210.05274/) (2022)
- [State-specific protein-ligand complex structure prediction with a multi-scale deep generative model](https://mlsanity.com/paper/2209.15171/) (2022)
- [End-to-end AI framework for interpretable prediction of molecular and crystal properties](https://mlsanity.com/paper/2212.11317/) (2022)

## Built on by (most cited first, 10 of 10)

- [MOFGPT: Generative Design of Metal-Organic Frameworks using Language Models](https://mlsanity.com/paper/2506.00198/) (2025)
- [AI-driven inverse design of materials: Past, present and future](https://mlsanity.com/paper/2411.09429/) (2024)
- [MOFSimBench: Evaluating Universal Machine Learning Interatomic Potentials In Metal--Organic Framework Molecular Modeling](https://mlsanity.com/paper/2507.11806/) (2025)
- [Mofasa: A Step Change in Metal-Organic Framework Generation](https://mlsanity.com/paper/2512.01756/) (2025)
- [Employing Artificial Intelligence to Steer Exascale Workflows with Colmena](https://mlsanity.com/paper/2408.14434/) (2024)
- [Diffusion-based Graph Generative Methods](https://mlsanity.com/paper/2401.15617/) (2024)
- [Property-guided Inverse Design of Metal-Organic Frameworks Using Quantum Natural Language Processing](https://mlsanity.com/paper/2405.11783/) (2024)
- [Steering an Active Learning Workflow Towards Novel Materials Discovery via Queue Prioritization](https://mlsanity.com/paper/2509.25538/) (2025)
- [Inverse Design of Inorganic Compounds with Generative AI](https://mlsanity.com/paper/2604.11827/) (2026)
- [Large language model agents accelerate inverse design of metal-organic frameworks for gas separation](https://mlsanity.com/paper/2607.10559/) (2026)

## Abstract (arXiv)

Metal-organic frameworks (MOFs) exhibit great promise for CO2 capture. However, finding the best performing materials poses computational and experimental grand challenges in view of the vast chemical space of potential building blocks. Here, we introduce GHP-MOFassemble, a generative artificial intelligence (AI), high performance framework for the rational and accelerated design of MOFs with high CO2 adsorption capacity and synthesizable linkers. GHP-MOFassemble generates novel linkers, assembled with one of three pre-selected metal nodes (Cu paddlewheel, Zn paddlewheel, Zn tetramer) into MOFs in a primitive cubic topology. GHP-MOFassemble screens and validates AI-generated MOFs for uniqueness, synthesizability, structural validity, uses molecular dynamics simulations to study their stability and chemical consistency, and crystal graph neural networks and Grand Canonical Monte Carlo simulations to quantify their CO2 adsorption capacities. We present the top six AI-generated MOFs with CO2 capacities greater than 2 m mol/g, i.e., higher than 96.9% of structures in the hypothetical MOF dataset.