Data-Driven Score-Based Models for Generating Stable Structures with Adaptive Crystal Cells

• URL: http://arxiv.org/abs/2310.10695v1
• Date: Mon, 16 Oct 2023 02:53:24 GMT
• Title: Data-Driven Score-Based Models for Generating Stable Structures with Adaptive Crystal Cells
• Authors: Arsen Sultanov, Jean-Claude Crivello, Tabea Rebafka, Nataliya Sokolovska
• Abstract summary: 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.
• Score: 1.515687944002438
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The discovery of new functional and stable materials is a big challenge due to its complexity. This work aims at the generation of new crystal structures with desired properties, such as chemical stability and specified chemical composition, by using machine learning generative models. Compared to the generation of molecules, crystal structures pose new difficulties arising from the periodic nature of the crystal and from the specific symmetry constraints related to the space group. In this work, score-based probabilistic models based on annealed Langevin dynamics, which have shown excellent performance in various applications, are adapted to the task of crystal generation. The novelty of the presented approach resides in the fact that the lattice of the crystal cell is not fixed. During the training of the model, the lattice is learned from the available data, whereas during the sampling of a new chemical structure, two denoising processes are used in parallel to generate the lattice along the generation of the atomic positions. A multigraph crystal representation is introduced that respects symmetry constraints, yielding computational advantages and a better quality of the sampled structures. We show that our model is capable of generating new candidate structures in any chosen chemical system and crystal group without any additional training. To illustrate the functionality of the proposed method, a comparison of our model to other recent generative models, based on descriptor-based metrics, is provided.

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)
• Generative Inverse Design of Crystal Structures via Diffusion Models with Transformers [1.2289361708127877]
  New inorganic materials with promising properties pose a critical challenge, both scientifically and for industrial applications. Discovery of new inorganic materials with promising properties poses a critical challenge, both scientifically and for industrial applications. In this study, we explore a new type of diffusion model for the generative inverse design of crystal structures, with a backbone based on a Transformer architecture.
  arXiv  Detail & Related papers  (2024-06-13T16:03:15Z)
• Efficient Probabilistic Modeling of Crystallization at Mesoscopic Scale [4.271235935891555]
  Crystallization processes at the mesoscopic scale are of particular interest in materials science and metallurgy. We introduce the Crystal Growth Neural Emulator (CGNE), a probabilistic model for efficient crystal growth at the mesoscopic scale. CGNE delivers a factor of 11 improvement in inference time and performance gains compared with recent state-of-the-art probabilistic models for dynamical systems.
  arXiv  Detail & Related papers  (2024-05-26T15:37:19Z)
• Complete and Efficient Graph Transformers for Crystal Material Property Prediction [53.32754046881189]
  Crystal structures are characterized by atomic bases within a primitive unit cell that repeats along a regular lattice throughout 3D space. We introduce a novel approach that utilizes the periodic patterns of unit cells to establish the lattice-based representation for each atom. We propose ComFormer, a SE(3) transformer designed specifically for crystalline materials.
  arXiv  Detail & Related papers  (2024-03-18T15:06:37Z)
• 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)
• Crystal-GFN: sampling crystals with desirable properties and constraints [103.79058968784163]
  We introduce Crystal-GFN, a generative model of crystal structures that sequentially samples structural properties of crystalline materials. In this paper, we use as objective the formation energy per atom of a crystal structure predicted by a new proxy machine learning model trained on MatBench. The results demonstrate that Crystal-GFN is able to sample highly diverse crystals with low (median -3.1 eV/atom) predicted formation energy.
  arXiv  Detail & Related papers  (2023-10-07T21:36:55Z)
• Unified Model for Crystalline Material Generation [9.940728137241214]
  We propose two unified models that act at the same time on crystal lattice and atomic positions. Our models are capable to learn any arbitrary crystal lattice deformation by lowering the total energy to reach thermodynamic stability.
  arXiv  Detail & Related papers  (2023-06-07T15:23:59Z)
• Equivariant Parameter Sharing for Porous Crystalline Materials [4.271235935891555]
  Existing methods for crystal property prediction either have constraints that are too restrictive or only incorporate symmetries between unit cells. We develop a model which incorporates the symmetries of the unit cell of a crystal in its architecture and explicitly models the porous structure. Our results confirm that our method performs better than existing methods for crystal property prediction and that the inclusion of symmetry results in a more efficient model.
  arXiv  Detail & Related papers  (2023-04-04T08:33:13Z)
• Learning Neural Generative Dynamics for Molecular Conformation Generation [89.03173504444415]
  We study how to generate molecule conformations (textiti.e., 3D structures) from a molecular graph. We propose a novel probabilistic framework to generate valid and diverse conformations given a molecular graph.
  arXiv  Detail & Related papers  (2021-02-20T03:17:58Z)

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.