FlowMM: Generating Materials with Riemannian Flow Matching

• URL: http://arxiv.org/abs/2406.04713v1
• Date: Fri, 7 Jun 2024 07:46:23 GMT
• Title: FlowMM: Generating Materials with Riemannian Flow Matching
• Authors: Benjamin Kurt Miller, Ricky T. Q. Chen, Anuroop Sriram, Brandon M Wood,
• Abstract summary: We present FlowMM, a pair of generative models that achieve state-of-the-art performance on both tasks. Our framework enables the freedom to choose the flow base distributions, drastically simplifying the problem of learning crystal structures. In addition to standard benchmarks, we validate FlowMM's generated structures with quantum chemistry calculations.
• Score: 16.68310253042657
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Crystalline materials are a fundamental component in next-generation technologies, yet modeling their distribution presents unique computational challenges. Of the plausible arrangements of atoms in a periodic lattice only a vanishingly small percentage are thermodynamically stable, which is a key indicator of the materials that can be experimentally realized. Two fundamental tasks in this area are to (a) predict the stable crystal structure of a known composition of elements and (b) propose novel compositions along with their stable structures. We present FlowMM, a pair of generative models that achieve state-of-the-art performance on both tasks while being more efficient and more flexible than competing methods. We generalize Riemannian Flow Matching to suit the symmetries inherent to crystals: translation, rotation, permutation, and periodic boundary conditions. Our framework enables the freedom to choose the flow base distributions, drastically simplifying the problem of learning crystal structures compared with diffusion models. In addition to standard benchmarks, we validate FlowMM's generated structures with quantum chemistry calculations, demonstrating that it is about 3x more efficient, in terms of integration steps, at finding stable materials compared to previous open methods.

Related papers

• Efficient Symmetry-Aware Materials Generation via Hierarchical Generative Flow Networks [52.13486402193811]
  New solid-state materials require rapidly exploring the vast space of crystal structures and locating stable regions. Existing methods struggle to explore large material spaces and generate diverse samples with desired properties and requirements. We propose a novel generative model employing a hierarchical exploration strategy to efficiently exploit the symmetry of the materials space to generate crystal structures given desired properties.
  arXiv  Detail & Related papers  (2024-11-06T23:53:34Z)
• FlowLLM: Flow Matching for Material Generation with Large Language Models as Base Distributions [16.68310253042657]
  FlowLLM is a novel generative model that combines large language models (LLMs) and Riemannian flow matching (RFM) to design novel crystalline materials. Our approach significantly outperforms state-of-the-art methods, increasing the generation rate of stable materials by over three times and increasing the rate for stable, unique, and novel crystals by $sim50%$.
  arXiv  Detail & Related papers  (2024-10-30T19:15:43Z)
• Scalable Diffusion for Materials Generation [99.71001883652211]
  We develop a unified crystal representation that can represent any crystal structure (UniMat) UniMat can generate high fidelity crystal structures from larger and more complex chemical systems. We propose additional metrics for evaluating generative models of materials.
  arXiv  Detail & Related papers  (2023-10-18T15:49:39Z)
• Latent Conservative Objective Models for Data-Driven Crystal Structure Prediction [62.36797874900395]
  In computational chemistry, crystal structure prediction is an optimization problem. One approach to tackle this problem involves building simulators based on density functional theory (DFT) followed by running search in simulation. We show that our approach, dubbed LCOMs (latent conservative objective models), performs comparably to the best current approaches in terms of success rate of structure prediction.
  arXiv  Detail & Related papers  (2023-10-16T04:35:44Z)
• Data-Driven Score-Based Models for Generating Stable Structures with Adaptive Crystal Cells [1.515687944002438]
  This work aims at the generation of new crystal structures with desired properties, such as chemical stability and specified chemical composition. The novelty of the presented approach resides in the fact that the lattice of the crystal cell is not fixed. A multigraph crystal representation is introduced that respects symmetry constraints, yielding computational advantages.
  arXiv  Detail & Related papers  (2023-10-16T02:53:24Z)
• Normalizing flows for atomic solids [67.70049117614325]
  We present a machine-learning approach, based on normalizing flows, for modelling atomic solids. We report Helmholtz free energy estimates for cubic and hexagonal ice modelled as monatomic water as well as for a truncated and shifted Lennard-Jones system. Our results thus demonstrate that normalizing flows can provide high-quality samples and free energy estimates of solids, without the need for multi-staging or for imposing restrictions on the crystal geometry.
  arXiv  Detail & Related papers  (2021-11-16T18:54:49Z)
• A deep learning driven pseudospectral PCE based FFT homogenization algorithm for complex microstructures [68.8204255655161]
  It is shown that the proposed method is able to predict central moments of interest while being magnitudes faster to evaluate than traditional approaches. It is shown, that the proposed method is able to predict central moments of interest while being magnitudes faster to evaluate than traditional approaches.
  arXiv  Detail & Related papers  (2021-10-26T07:02:14Z)
• Crystal Diffusion Variational Autoencoder for Periodic Material Generation [29.558155407825115]
  We propose a Crystal Diffusion Variational Autoencoder (CDVAE) that captures the inductive bias of material stability. By learning from the data distribution of stable materials, the decoder generates materials in a diffusion process that moves atomic coordinates towards a lower energy state. We significantly outperform past methods in three tasks: 1) reconstructing the input structure, 2) generating valid, diverse, and realistic materials, and 3) generating materials that optimize a specific property.
  arXiv  Detail & Related papers  (2021-10-12T17:49:49Z)
• Autonomous Optimization of Fluid Systems at Varying Length Scales [55.41644538483948]
  We propose a computer vision-driven Bayesian optimization framework to discover the precise hardware conditions that generate uniform droplets. This framework is validated on two fluid systems, at the micrometer and millimeter length scales, using microfluidic and inkjet systems.
  arXiv  Detail & Related papers  (2021-05-28T02:08:03Z)

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.