Crystal Diffusion Variational Autoencoder for Periodic Material Generation

• URL: http://arxiv.org/abs/2110.06197v1
• Date: Tue, 12 Oct 2021 17:49:49 GMT
• Title: Crystal Diffusion Variational Autoencoder for Periodic Material Generation
• Authors: Tian Xie, Xiang Fu, Octavian-Eugen Ganea, Regina Barzilay, Tommi Jaakkola
• Abstract summary: 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.
• Score: 29.558155407825115
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Generating the periodic structure of stable materials is a long-standing challenge for the material design community. This task is difficult because stable materials only exist in a low-dimensional subspace of all possible periodic arrangements of atoms: 1) the coordinates must lie in the local energy minimum defined by quantum mechanics, and 2) global stability also requires the structure to follow the complex, yet specific bonding preferences between different atom types. Existing methods fail to incorporate these factors and often lack proper invariances. We propose a Crystal Diffusion Variational Autoencoder (CDVAE) that captures the physical 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 and updates atom types to satisfy bonding preferences between neighbors. Our model also explicitly encodes interactions across periodic boundaries and respects permutation, translation, rotation, and periodic invariances. 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. We also provide several standard datasets and evaluation metrics for the broader machine learning community.

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)
• Unleashing the power of novel conditional generative approaches for new materials discovery [3.972733741872872]
  We propose two generative approaches to the problem of crystal structure design. One is conditional structure modification, using the energy difference between the most energetically favorable structure and all its less stable polymorphs. The other is conditional structure generation, using the energy difference between the most energetically favorable structure and all its less stable polymorphs.
  arXiv  Detail & Related papers  (2024-11-05T14:58:31Z)
• FlowMM: Generating Materials with Riemannian Flow Matching [16.68310253042657]
  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.
  arXiv  Detail & Related papers  (2024-06-07T07:46:23Z)
• Response Matching for generating materials and molecules [0.0]
  We present a novel generative method called Response Matching (RM) RM exploits the locality of atomic interactions, and inherently respects permutation, translation, rotation, and periodic invariances. We demonstrate the efficiency and generalization of RM across three systems.
  arXiv  Detail & Related papers  (2024-05-15T03:08:21Z)
• 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)
• Fold2Seq: A Joint Sequence(1D)-Fold(3D) Embedding-based Generative Model for Protein Design [70.27706384570723]
  We propose Fold2Seq, a novel framework for designing protein sequences conditioned on a specific target fold. We show improved or comparable performance of Fold2Seq in terms of speed, coverage, and reliability for sequence design. The unique advantages of fold-based Fold2Seq, in comparison to a structure-based deep model and RosettaDesign, become more evident on three additional real-world challenges.
  arXiv  Detail & Related papers  (2021-06-24T14:34:24Z)
• SPANet: Generalized Permutationless Set Assignment for Particle Physics using Symmetry Preserving Attention [62.43586180025247]
  Collisions at the Large Hadron Collider produce variable-size sets of observed particles. Physical symmetries of decay products complicate assignment of observed particles to decay products. We introduce a novel method for constructing symmetry-preserving attention networks.
  arXiv  Detail & Related papers  (2021-06-07T18:18:20Z)
• Predicting Material Properties Using a 3D Graph Neural Network with Invariant Local Descriptors [0.4956709222278243]
  Accurately predicting material properties is critical for discovering and designing novel materials. Among the machine learning methods, graph convolution neural networks (GCNNs) have been one of the most successful ones. We propose an adaptive GCNN with novel convolutions that model interactions among all neighboring atoms in three-dimensional space simultaneously.
  arXiv  Detail & Related papers  (2021-02-16T19:56:54Z)
• 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.