mlsanityData to 14 Oct 2026

Accurate transition state generation with an object-aware equivariant elementary reaction diffusion model

arXiv 2304.06174 ↗First posted 12 Apr 2023physics.chem-ph cs.LGcited by 30

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Abstract (arXiv)

Transition state (TS) search is key in chemistry for elucidating reaction mechanisms and exploring reaction networks. The search for accurate 3D TS structures, however, requires numerous computationally intensive quantum chemistry calculations due to the complexity of potential energy surfaces. Here, we developed an object-aware SE(3) equivariant diffusion model that satisfies all physical symmetries and constraints for generating sets of structures - reactant, TS, and product - in an elementary reaction. Provided reactant and product, this model generates a TS structure in seconds instead of hours required when performing quantum chemistry-based optimizations. The generated TS structures achieve a median of 0.08 AA root mean square deviation compared to the true TS. With a confidence scoring model for uncertainty quantification, we approach an accuracy required for reaction rate estimation (2.6 kcal/mol) by only performing quantum chemistry-based optimizations on 14% of the most challenging reactions. We envision the proposed approach useful in constructing large reaction networks with unknown mechanisms.

Key facts. Cited by 30 papers in the map (data through 14 Oct 2026). Builds on 23 of its 68 references that are in the map. First posted on arXiv 12 Apr 2023.

Builds on

23 of 68 references.

References · 23

  1. 2020Denoising Diffusion Probabilistic Modelscited by 21,076
  2. 2020Score-Based Generative Modeling through Stochastic Differential Equationscited by 9,122
  3. 2015Deep Unsupervised Learning using Nonequilibrium Thermodynamicscited by 7,058
  4. 2022Flow Matching for Generative Modelingcited by 5,702
  5. 2021Improved Denoising Diffusion Probabilistic Modelscited by 3,761
  6. 2017Neural Message Passing for Quantum Chemistrycited by 3,487
  7. 2022RePaint: Inpainting using Denoising Diffusion Probabilistic Modelscited by 1,430
  8. 2021Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gaugescited by 1,167
  9. 2021E(n) Equivariant Graph Neural Networkscited by 965
  10. 2018Tensor field networks: Rotation- and translation-equivariant neural networks for 3D point cloudscited by 746
  11. 2021E(3)-Equivariant Graph Neural Networks for Data-Efficient and Accurate Interatomic Potentialscited by 708
  12. 2022Equivariant Diffusion for Molecule Generation in 3Dcited by 677
11 more
  1. 2022DiffDock: Diffusion Steps, Twists, and Turns for Molecular Dockingcited by 313
  2. 2020Equivariant Flows: Exact Likelihood Generative Learning for Symmetric Densitiescited by 250
  3. 2023I2SB: Image-to-Image Schr"odinger Bridgecited by 218
  4. 2022Structure-based Drug Design with Equivariant Diffusion Modelscited by 217
  5. 2021SE(3) Equivariant Graph Neural Networks with Complete Local Framescited by 76
  6. 2023A new perspective on building efficient and expressive 3D equivariant graph neural networkscited by 47
  7. 2022Transition1x -- a Dataset for Building Generalizable Reactive Machine Learning Potentialscited by 37
  8. 2023A Theoretical Justification for Image Inpainting using Denoising Diffusion Probabilistic Modelscited by 37
  9. 2020Deep Reinforcement Learning of Transition Statescited by 8
  10. 2022NeuralNEB -- Neural Networks can find Reaction Paths Fastcited by 7
  11. 2022A Transferable Recommender Approach for Selecting the Best Density Functional Approximations in Chemical Discoverycited by 3

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 3; 2024: 12; 2025: 8; 2026: 7.

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  1. 2025Diffusion Generative Modeling for Spatially Resolved Gene Expression Inference from Histology Imagescited by 37
  2. 2023RetroBridge: Modeling Retrosynthesis with Markov Bridgescited by 36
  3. 2024A Survey of Geometric Graph Neural Networks: Data Structures, Models and Applicationscited by 28
  4. 2024Evaluating the design space of diffusion-based generative modelscited by 26
  5. 2023Diffusion-based Generative AI for Exploring Transition States from 2D Molecular Graphscited by 19
  6. 2025RNE: plug-and-play diffusion inference-time control and energy-based trainingcited by 17
  7. 2025Electron flow matching for generative reaction mechanism prediction obeying conservation lawscited by 13
  8. 2025CREPE: Controlling Diffusion with Replica Exchangecited by 11
  9. 2024Transition Path Sampling with Improved Off-Policy Training of Diffusion Path Samplerscited by 10
  10. 2024Quantum State Generation with Structure-Preserving Diffusion Modelcited by 8
  11. 20233DReact: Geometric deep learning for chemical reactionscited by 7
  12. 2024Generalized Flow Matching for Transition Dynamics Modelingcited by 4
18 more
  1. 2025Building-Block Aware Generative Modeling for 3D Crystals of Metal Organic Frameworkscited by 4
  2. 2024React-OT: Optimal Transport for Generating Transition State in Chemical Reactionscited by 3
  3. 2024Active learning of Boltzmann samplers and potential energies with quantum mechanical accuracycited by 2
  4. 2024Diff-PIC: Revolutionizing Particle-In-Cell Nuclear Fusion Simulation with Diffusion Modelscited by 2
  5. 2025Transferable Learning of Reaction Pathways from Geometric Priorscited by 2
  6. 2025Flow matching for reaction pathway generationcited by 2
  7. 2026FragmentFlow: Scalable Transition State Generation for Large Moleculescited by 2
  8. 2024Estimating Reaction Barriers with Deep Reinforcement Learningcited by 1
  9. 2024Grand canonical generative diffusion model for crystalline phases and grain boundariescited by 1
  10. 2024Riemannian Denoising Model for Molecular Structure Optimization with Chemical Accuracycited by 1
  11. 2024Generative modeling assisted simulation of measurement-altered quantum criticalitycited by 0
  12. 2025Accelerating High-Throughput Catalyst Screening by Direct Generation of Equilibrium Adsorption Structurescited by 0
  13. 2026A Priori Sampling of Transition States with Guided Diffusioncited by 0
  14. 2026SymDrift: One-Shot Generative Modeling under Symmetriescited by 0
  15. 2026ElemeNet: Multiscale Molecular Machine Learning with Uncertainty Quantification Across the Periodic Tablecited by 0
  16. 2026Reaction-Transformation-Aware Flow Matching for Generalizable Transition State Generationcited by 0
  17. 2026ReCurveflow: A Flow Matching Framework that Learns Curved Reaction Trajectories to Predict Transition State Geometriescited by 0
  18. 2026Error-Corrected Inference-Time Scaling for Imperfect Diffusion Modelscited by 0

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  1. 2025Generating transition states of chemical reactions via distance-geometry-based flow matching
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  3. 2024Chemistry-Inspired Diffusion with Non-Differentiable Guidance
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  5. 2023Navigating the Design Space of Equivariant Diffusion-Based Generative Models for De Novo 3D Molecule Generation
  6. 2026Domain-Gated Latent Diffusion: Generative Inverse Design of HMX-Class Energetic Materials with First-Principles Validation
  7. 2023Investigating the Behavior of Diffusion Models for Accelerating Electronic Structure Calculations
  8. 2026Order Matters in Retrosynthesis: Structure-aware Generation via Reaction-Center-Guided Discrete Flow Matching