Mofasa: A Step Change in Metal-Organic Framework Generation

TL;DR

Mofasa is a novel all-atom latent diffusion model that significantly advances the generation of Metal-Organic Frameworks, enabling the discovery of diverse, high-quality MOF structures without handcrafted algorithms.

Contribution

We introduce Mofasa, a state-of-the-art generative model for MOFs that jointly samples atomic positions, types, and lattice vectors for large systems, surpassing previous methods.

Findings

Achieves state-of-the-art performance in MOF generation

Supports systems with up to 500 atoms

Provides a large annotated database for community use

Abstract

Mofasa is an all-atom latent diffusion model with state-of-the-art performance for generating Metal-Organic Frameworks (MOFs). These are highly porous crystalline materials used to harvest water from desert air, capture carbon dioxide, store toxic gases and catalyse chemical reactions. In recognition of their value, the development of MOFs recently received a Nobel Prize in Chemistry. In many ways, MOFs are well-suited for exploiting generative models in chemistry: they are rationally-designable materials with a large combinatorial design space and strong structure-property couplings. And yet, to date, a high performance generative model has been lacking. To fill this gap, we introduce Mofasa, a general-purpose latent diffusion model that jointly samples positions, atom-types and lattice vectors for systems as large as 500 atoms. Mofasa avoids handcrafted assembly algorithms common in the literature, unlocking the simultaneous discovery of metal nodes, linkers and topologies. To help the scientific community build on our work, we release MofasaDB, an annotated library of hundreds of thousands of sampled MOF structures, along with a user-friendly web interface for search and discovery: https://mofux.ai/ .