MOFFlow: Flow Matching for Structure Prediction of Metal-Organic Frameworks

• URL: http://arxiv.org/abs/2410.17270v1
• Date: Mon, 07 Oct 2024 13:51:58 GMT
• Title: MOFFlow: Flow Matching for Structure Prediction of Metal-Organic Frameworks
• Authors: Nayoung Kim, Seongsu Kim, Minsu Kim, Jinkyoo Park, Sungsoo Ahn,
• Abstract summary: Metal-organic frameworks (MOFs) are a class of crystalline materials with promising applications in many areas such as carbon capture and drug delivery. Existing approaches, including ab initio calculations and even deep generative models, struggle with the complexity of MOF structures due to the large number of atoms in the unit cells. We introduce MOFFlow, the first deep generative model tailored for MOF structure prediction.
• Score: 42.61784133509237
• License:
• Abstract: Metal-organic frameworks (MOFs) are a class of crystalline materials with promising applications in many areas such as carbon capture and drug delivery. In this work, we introduce MOFFlow, the first deep generative model tailored for MOF structure prediction. Existing approaches, including ab initio calculations and even deep generative models, struggle with the complexity of MOF structures due to the large number of atoms in the unit cells. To address this limitation, we propose a novel Riemannian flow matching framework that reduces the dimensionality of the problem by treating the metal nodes and organic linkers as rigid bodies, capitalizing on the inherent modularity of MOFs. By operating in the $SE(3)$ space, MOFFlow effectively captures the roto-translational dynamics of these rigid components in a scalable way. Our experiment demonstrates that MOFFlow accurately predicts MOF structures containing several hundred atoms, significantly outperforming conventional methods and state-of-the-art machine learning baselines while being much faster.

Related papers

• Searching for Efficient Linear Layers over a Continuous Space of Structured Matrices [88.33936714942996]
  We present a unifying framework that enables searching among all linear operators expressible via an Einstein summation. We show that differences in the compute-optimal scaling laws are mostly governed by a small number of variables. We find that Mixture-of-Experts (MoE) learns an MoE in every single linear layer of the model, including the projection in the attention blocks.
  arXiv  Detail & Related papers  (2024-10-03T00:44:50Z)
• MOFDiff: Coarse-grained Diffusion for Metal-Organic Framework Design [4.819734936375677]
  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.
  arXiv  Detail & Related papers  (2023-10-16T18:00:15Z)
• 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)
• 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)
• Multi-Task Mixture Density Graph Neural Networks for Predicting Cu-based Single-Atom Alloy Catalysts for CO2 Reduction Reaction [61.9212585617803]
  Graph neural networks (GNNs) have drawn more and more attention from material scientists. We develop a multi-task (MT) architecture based on DimeNet++ and mixture density networks to improve the performance of such task.
  arXiv  Detail & Related papers  (2022-09-15T13:52:15Z)
• 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)
• Extended Unconstrained Features Model for Exploring Deep Neural Collapse [59.59039125375527]
  Recently, a phenomenon termed "neural collapse" (NC) has been empirically observed in deep neural networks. Recent papers have shown that minimizers with this structure emerge when optimizing a simplified "unconstrained features model" In this paper, we study the UFM for the regularized MSE loss, and show that the minimizers' features can be more structured than in the cross-entropy case.
  arXiv  Detail & Related papers  (2022-02-16T14:17:37Z)
• Using Machine Learning and Data Mining to Leverage Community Knowledge for the Engineering of Stable Metal-Organic Frameworks [0.9187159782788578]
  MOFs hold promise for engineering challenges ranging from gas separations to stability. To overcome limitation, we extract thousands of published reports of the key aspects of MOF stability necessary for their practical application. We use natural language processing and automated image analysis to obtain over 2,000 solvent-removal measures and 3,000 thermal temperatures.
  arXiv  Detail & Related papers  (2021-06-24T21:35:26Z)
• 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.