Crystal-GFN: sampling crystals with desirable properties and constraints

• URL: http://arxiv.org/abs/2310.04925v2
• Date: Wed, 13 Dec 2023 16:24:44 GMT
• Title: Crystal-GFN: sampling crystals with desirable properties and constraints
• Authors: Mila AI4Science and Alex Hernandez-Garcia and Alexandre Duval and Alexandra Volokhova and Yoshua Bengio and Divya Sharma and Pierre Luc Carrier and Yasmine Benabed and Micha{\l} Koziarski and Victor Schmidt
• Abstract summary: 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.
• Score: 103.79058968784163
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Accelerating material discovery holds the potential to greatly help mitigate the climate crisis. Discovering new solid-state materials such as electrocatalysts, super-ionic conductors or photovoltaic materials can have a crucial impact, for instance, in improving the efficiency of renewable energy production and storage. In this paper, we introduce Crystal-GFN, a generative model of crystal structures that sequentially samples structural properties of crystalline materials, namely the space group, composition and lattice parameters. This domain-inspired approach enables the flexible incorporation of physical and structural hard constraints, as well as the use of any available predictive model of a desired physicochemical property as an objective function. To design stable materials, one must target the candidates with the lowest formation energy. Here, 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.

Related papers

• Space Group Informed Transformer for Crystalline Materials Generation [2.405914457225118]
  We introduce CrystalFormer, a transformer-based autoregressive model for space group-controlled generation of crystalline materials. CrystalFormer learns to generate crystals by directly predicting the species and locations of symmetry-inequivalent atoms in the unit cell. Our results demonstrate that CrystalFormer matches state-of-the-art performance on standard benchmarks for both validity, novelty, and stability of the generated crystalline materials.
  arXiv  Detail & Related papers  (2024-03-23T06:01:45Z)
• 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)
• 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)
• 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)
• 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)
• Neural Structure Fields with Application to Crystal Structure Autoencoders [10.680545976155173]
  We propose neural structure fields (NeSF) as an accurate and practical approach for representing crystal structures using neural networks. NeSF overcomes the tradeoff between spatial resolution and computational complexity and can represent any crystal structure. We propose an autoencoder of crystal structures that can recover various crystal structures, such as those of perovskite structure materials and cuprate superconductors.
  arXiv  Detail & Related papers  (2022-12-08T16:41:41Z)
• 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)
• BIGDML: Towards Exact Machine Learning Force Fields for Materials [55.944221055171276]
  Machine-learning force fields (MLFF) should be accurate, computationally and data efficient, and applicable to molecules, materials, and interfaces thereof. Here, we introduce the Bravais-Inspired Gradient-Domain Machine Learning approach and demonstrate its ability to construct reliable force fields using a training set with just 10-200 atoms.
  arXiv  Detail & Related papers  (2021-06-08T10:14:57Z)

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.