MOFDiff: Coarse-grained Diffusion for Metal-Organic Framework Design
- URL: http://arxiv.org/abs/2310.10732v1
- Date: Mon, 16 Oct 2023 18:00:15 GMT
- Title: MOFDiff: Coarse-grained Diffusion for Metal-Organic Framework Design
- Authors: Xiang Fu, Tian Xie, Andrew S. Rosen, Tommi Jaakkola, Jake Smith
- Abstract summary: Metal-organic frameworks (MOFs) are of immense interest in applications such as gas storage and carbon capture.
We propose MOFDiff: a coarse-grained (CG) diffusion model that generates CG MOF structures.
We evaluate our model's capability to generate valid and novel MOF structures and its effectiveness in designing outstanding MOF materials.
- Score: 4.819734936375677
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Metal-organic frameworks (MOFs) are of immense interest in applications such
as gas storage and carbon capture due to their exceptional porosity and tunable
chemistry. Their modular nature has enabled the use of template-based methods
to generate hypothetical MOFs by combining molecular building blocks in
accordance with known network topologies. However, the ability of these methods
to identify top-performing MOFs is often hindered by the limited diversity of
the resulting chemical space. In this work, we propose MOFDiff: a
coarse-grained (CG) diffusion model that generates CG MOF structures through a
denoising diffusion process over the coordinates and identities of the building
blocks. The all-atom MOF structure is then determined through a novel assembly
algorithm. Equivariant graph neural networks are used for the diffusion model
to respect the permutational and roto-translational symmetries. We
comprehensively evaluate our model's capability to generate valid and novel MOF
structures and its effectiveness in designing outstanding MOF materials for
carbon capture applications with molecular simulations.
Related papers
- AUTODIFF: Autoregressive Diffusion Modeling for Structure-based Drug Design [16.946648071157618]
We propose a diffusion-based fragment-wise autoregressive generation model for structure-based drug design (SBDD)
We design a novel molecule assembly strategy named conformal motif that preserves the conformation of local structures of molecules first.
We then encode the interaction of the protein-ligand complex with an SE(3)-equivariant convolutional network and generate molecules motif-by-motif with diffusion modeling.
arXiv Detail & Related papers (2024-04-02T14:44:02Z) - MolNexTR: A Generalized Deep Learning Model for Molecular Image
Recognition [4.7793786389946815]
MolNexTR is a novel image-to-graph model that collaborates to fuse the strengths of ConvNext and Vision-TRansformer.
It can predict atoms and bonds simultaneously and understand their layout rules.
MolNexTR has demonstrated superior performance, achieving an accuracy rate of 81-97%.
arXiv Detail & Related papers (2024-03-06T13:17:41Z) - A generative artificial intelligence framework based on a molecular
diffusion model for the design of metal-organic frameworks for carbon capture [3.7693836475281297]
GHP-MOFassemble is a generative artificial intelligence framework for the rational and accelerated design of MOFs with high CO2 capacity and synthesizable linkers.
GHP-MOFassemble screens and validates AI-generated MOFs for uniqueness, synthesizability, structural validity.
We present the top six AI-generated MOFs with CO2 capacities greater than 2 $m mol/g$, higher than 96.9% of structures in the hypothetical MOF dataset.
arXiv Detail & Related papers (2023-06-14T18:32:26Z) - Molecule Design by Latent Space Energy-Based Modeling and Gradual
Distribution Shifting [53.44684898432997]
Generation of molecules with desired chemical and biological properties is critical for drug discovery.
We propose a probabilistic generative model to capture the joint distribution of molecules and their properties.
Our method achieves very strong performances on various molecule design tasks.
arXiv Detail & Related papers (2023-06-09T03:04:21Z) - Towards Predicting Equilibrium Distributions for Molecular Systems with
Deep Learning [60.02391969049972]
We introduce a novel deep learning framework, called Distributional Graphormer (DiG), in an attempt to predict the equilibrium distribution of molecular systems.
DiG employs deep neural networks to transform a simple distribution towards the equilibrium distribution, conditioned on a descriptor of a molecular system.
arXiv Detail & Related papers (2023-06-08T17:12:08Z) - Geometric Latent Diffusion Models for 3D Molecule Generation [172.15028281732737]
Generative models, especially diffusion models (DMs), have achieved promising results for generating feature-rich geometries.
We propose a novel and principled method for 3D molecule generation named Geometric Latent Diffusion Models (GeoLDM)
arXiv Detail & Related papers (2023-05-02T01:07:22Z) - MOFormer: Self-Supervised Transformer model for Metal-Organic Framework
Property Prediction [7.367477168940467]
Metal-Organic Frameworks (MOFs) are materials with a high degree of porosity that can be used for applications in energy storage, water desalination, gas storage, and gas separation.
Finding the optimal MOFs for specific applications requires an efficient and accurate search over an enormous number of potential candidates.
We propose a structure-agnostic deep learning method based on the Transformer model, named as MOFormer, for property predictions of MOFs.
arXiv Detail & Related papers (2022-10-25T17:29:42Z) - Building Open Knowledge Graph for Metal-Organic Frameworks (MOF-KG):
Challenges and Case Studies [63.61566811532431]
Metal-Organic Frameworks (MOFs) have great potential to revolutionize applications such as gas storage, molecular separations, chemical sensing, crystalline and drug delivery.
The Cambridge Structural Database (CSD) reports 10,636 synthesized MOF crystals which in addition contains ca. 114,373 MOF-like structures.
In this demo paper, we describe our effort on leveraging knowledge graph methods to facilitate MOF prediction, discovery, and synthesis.
arXiv Detail & Related papers (2022-07-10T16:41:11Z) - Moser Flow: Divergence-based Generative Modeling on Manifolds [49.04974733536027]
Moser Flow (MF) is a new class of generative models within the family of continuous normalizing flows (CNF)
MF does not require invoking or backpropagating through an ODE solver during training.
We demonstrate for the first time the use of flow models for sampling from general curved surfaces.
arXiv Detail & Related papers (2021-08-18T09:00:24Z) - BIGDML: Towards Exact Machine Learning Force Fields for Materials [55.944221055171276]
Machine-learning force fields (MLFF) should be accurate, computationally and data efficient, and applicable to molecules, materials, and interfaces thereof.
Here, we introduce the Bravais-Inspired Gradient-Domain Machine Learning approach and demonstrate its ability to construct reliable force fields using a training set with just 10-200 atoms.
arXiv Detail & Related papers (2021-06-08T10:14:57Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.