Learning Ordering in Crystalline Materials with Symmetry-Aware Graph Neural Networks

• URL: http://arxiv.org/abs/2409.13851v1
• Date: Fri, 20 Sep 2024 18:53:48 GMT
• Title: Learning Ordering in Crystalline Materials with Symmetry-Aware Graph Neural Networks
• Authors: Jiayu Peng, James Damewood, Jessica Karaguesian, Jaclyn R. Lunger, Rafael Gómez-Bombarelli,
• Abstract summary: Graphal neural networks (GCNNs) have become a machine learning workhorse for screening the chemical space of crystalline materials. We benchmark various neural network architectures for capturing the ordering-dependent energetics of multicomponent materials.
• Score: 0.836362570897926
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Graph convolutional neural networks (GCNNs) have become a machine learning workhorse for screening the chemical space of crystalline materials in fields such as catalysis and energy storage, by predicting properties from structures. Multicomponent materials, however, present a unique challenge since they can exhibit chemical (dis)order, where a given lattice structure can encompass a variety of elemental arrangements ranging from highly ordered structures to fully disordered solid solutions. Critically, properties like stability, strength, and catalytic performance depend not only on structures but also on orderings. To enable rigorous materials design, it is thus critical to ensure GCNNs are capable of distinguishing among atomic orderings. However, the ordering-aware capability of GCNNs has been poorly understood. Here, we benchmark various neural network architectures for capturing the ordering-dependent energetics of multicomponent materials in a custom-made dataset generated with high-throughput atomistic simulations. Conventional symmetry-invariant GCNNs were found unable to discern the structural difference between the diverse symmetrically inequivalent atomic orderings of the same material, while symmetry-equivariant model architectures could inherently preserve and differentiate the distinct crystallographic symmetries of various orderings.

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)
• Exploring structure diversity in atomic resolution microscopy with graph neural networks [18.903519247639355]
  Deep learning is a powerful tool to explore structure diversity in a fast, accurate, and intelligent manner. This work provides a powerful tool to explore structure diversity in a fast, accurate, and intelligent manner.
  arXiv  Detail & Related papers  (2024-10-23T07:48:35Z)
• Equivariant graph convolutional neural networks for the representation of homogenized anisotropic microstructural mechanical response [1.283555556182245]
  Composite materials with different microstructural material symmetries are common in engineering applications. We provide neural network architectures that provide effective homogenization models of materials with anisotropic components.
  arXiv  Detail & Related papers  (2024-04-05T14:49:01Z)
• Crystalformer: Infinitely Connected Attention for Periodic Structure Encoding [10.170537065646323]
  Predicting physical properties of materials from their crystal structures is a fundamental problem in materials science. We show that crystal structures are infinitely repeating, periodic arrangements of atoms, whose fully connected attention results in infinitely connected attention. We propose a simple yet effective Transformer-based encoder architecture for crystal structures called Crystalformer.
  arXiv  Detail & Related papers  (2024-03-18T11:37:42Z)
• 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)
• Towards Symmetry-Aware Generation of Periodic Materials [64.21777911715267]
  We propose SyMat, a novel material generation approach that can capture physical symmetries of periodic material structures. SyMat generates atom types and lattices of materials through generating atom type sets, lattice lengths and lattice angles with a variational auto-encoder model. We show that SyMat is theoretically invariant to all symmetry transformations on materials and demonstrate that SyMat achieves promising performance on random generation and property optimization tasks.
  arXiv  Detail & Related papers  (2023-07-06T01:05:34Z)
• Differentiable graph-structured models for inverse design of lattice materials [0.0]
  Architected materials possessing physico-chemical properties adaptable to disparate environmental conditions embody a disruptive new domain of materials science. We propose a new computational approach using graph-based representation for regular and irregular lattice materials.
  arXiv  Detail & Related papers  (2023-04-11T18:00:21Z)
• Universal approximation property of invertible neural networks [76.95927093274392]
  Invertible neural networks (INNs) are neural network architectures with invertibility by design. Thanks to their invertibility and the tractability of Jacobian, INNs have various machine learning applications such as probabilistic modeling, generative modeling, and representation learning.
  arXiv  Detail & Related papers  (2022-04-15T10:45:26Z)
• 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)
• Orbital Graph Convolutional Neural Network for Material Property Prediction [0.0]
  We propose the Orbital Graph Convolutional Neural Network (OGCNN), a crystal graph convolutional neural network framework. OGCNN includes atomic orbital interaction features that learn material properties in a robust way. We examined the performance of this model on a broad range of crystalline material data to predict different properties.
  arXiv  Detail & Related papers  (2020-08-14T15:22:22Z)
• Graph Neural Network for Hamiltonian-Based Material Property Prediction [56.94118357003096]
  We present and compare several different graph convolution networks that are able to predict the band gap for inorganic materials. The models are developed to incorporate two different features: the information of each orbital itself and the interaction between each other. The results show that our model can get a promising prediction accuracy with cross-validation.
  arXiv  Detail & Related papers  (2020-05-27T13:32:10Z)

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.