MiAD: Mirage Atom Diffusion for De Novo Crystal Generation
On the map: Diffusion models → Flow matching, time series and science
Abstract (arXiv)
In recent years, diffusion-based models have demonstrated exceptional performance in searching for simultaneously stable, unique, and novel (S.U.N.) crystalline materials. However, most of these models don't have the ability to change the number of atoms in the crystal during the generation process, which limits the variability of model sampling trajectories. In this paper, we demonstrate the severity of this restriction and introduce a simple yet powerful technique, mirage infusion, which enables diffusion models to change the state of the atoms that make up the crystal from existent to non-existent (mirage) and vice versa. We show that this technique improves model quality by up to x2.5 compared to the same model without this modification. The resulting model, Mirage Atom Diffusion (MiAD), is an equivariant joint diffusion model for de novo crystal generation that is capable of altering the number of atoms during the generation process. MiAD achieves an 8.2% S.U.N. rate on the MP-20 dataset, which substantially exceeds existing state-of-the-art approaches. Code: https://github.com/andrey-okhotin/miad.git
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Placed in Flow matching, time series and science, one of 14 directions in Diffusion models. Builds on 36 of its 56 references that are in the map. First posted on arXiv 18 Nov 2025.
Builds on
36 of 56 references.
Closest ancestors · 9
Same direction, newest first
- 2025Multi-domain Distribution Learning for De Novo Drug Design25 Aug
- 2025All-atom Diffusion Transformers: Unified generative modelling of molecules and materials5 Mar
- 2025Periodic Materials Generation using Text-Guided Joint Diffusion Model1 Mar
- 2024FlowLLM: Flow Matching for Material Generation with Large Language Models as Base Distributions30 Oct
- 2023Crystal Structure Prediction by Joint Equivariant Diffusion30 Jul
- 2023Equivariant flow matching26 Jun
- 2023Flow Matching on General Geometries7 Feb
- 2022Flow Matching for Generative Modeling6 Oct
- 2022Equivariant Diffusion for Molecule Generation in 3D31 Mar
Other references · 27
- 2020Denoising Diffusion Probabilistic Modelscited by 21,076
- 2023LLaMA: Open and Efficient Foundation Language Modelscited by 14,132
- 2020Score-Based Generative Modeling through Stochastic Differential Equationscited by 9,122
- 2015Deep Unsupervised Learning using Nonequilibrium Thermodynamicscited by 7,058
- 2018A Style-Based Generator Architecture for Generative Adversarial Networkscited by 6,013
- 2021Structured Denoising Diffusion Models in Discrete State-Spacescited by 1,719
- 2021Score-based Generative Modeling in Latent Spacecited by 636
- 2021Argmax Flows and Multinomial Diffusion: Learning Categorical Distributionscited by 574
- 2021Tackling the Generative Learning Trilemma with Denoising Diffusion GANscited by 516
- 2021Crystal Diffusion Variational Autoencoder for Periodic Material Generationcited by 270
- 2022Riemannian Score-Based Generative Modellingcited by 219
- 2022Riemannian Diffusion Modelscited by 122
15 more
- 2024FlowMM: Generating Materials with Riemannian Flow Matchingcited by 109
- 2023MatterGen: a generative model for inorganic materials designcited by 98
- 2024Fine-Tuned Language Models Generate Stable Inorganic Materials as Textcited by 93
- 2024Space Group Constrained Crystal Generationcited by 73
- 2023Scalable Diffusion for Materials Generationcited by 54
- 2024Open Materials 2024 (OMat24) Inorganic Materials Dataset and Modelscited by 52
- 2025SymmCD: Symmetry-Preserving Crystal Generation with Diffusion Modelscited by 51
- 2024Space Group Informed Transformer for Crystalline Materials Generationcited by 41
- 2023Matbench Discovery -- A framework to evaluate machine learning crystal stability predictionscited by 35
- 2023Crystal-GFN: sampling crystals with desirable properties and constraintscited by 31
- 2025Wyckoff Transformer: Generation of Symmetric Crystalscited by 27
- 2025A Periodic Bayesian Flow for Material Generationcited by 16
- 2023Star-Shaped Denoising Diffusion Probabilistic Modelscited by 13
- 2025Symmetry-Aware Bayesian Flow Networks for Crystal Generationcited by 9
- 2024Efficient Symmetry-Aware Materials Generation via Hierarchical Generative Flow Networkscited by 3
Built on by
Citing papers per year: 2026: 4.
4 papers.
- 2026Zatom-1: Towards a Multimodal Foundation Model for 3D Molecules and Materialscited by 5
- 2026Symmetry-Breaking De Novo Crystal Generation via Markovian Jump Diffusioncited by 0
- 2026GLASS: Global Latent Aggregation with Slot-based Set Decoding for Scalable All-Atom Crystal Generationcited by 0
- 2026OxiGen: Oxidation-State-Aware Crystal Generationcited by 0
- 2026OxiGen: Oxidation-State-Aware Crystal Generation6 Oct 2026
- 2026GLASS: Global Latent Aggregation with Slot-based Set Decoding for Scalable All-Atom Crystal Generation29 Sep 2026
- 2026Symmetry-Breaking De Novo Crystal Generation via Markovian Jump Diffusion13 Aug 2026
- 2026Zatom-1: Towards a Multimodal Foundation Model for 3D Molecules and Materials24 Feb 2026
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