Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
SymDiff: Equivariant Diffusion via Stochastic Symmetrisation
Authors: Leo Zhang, Kianoosh Ashouritaklimi, Yee Whye Teh, Rob Cornish
ICLR 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We show that this additional flexibility yields significant empirical benefit for E(3)-equivariant molecular generation. To validate our framework, we implemented SYMDIFF for de novo molecular generation, and evaluated it as a drop-in replacement for the E(3)-equivariant diffusion of Hoogeboom et al. (2022), which relies on intrinsically equivariant neural networks. In contrast, our model is able to leverage highly scalable off-the-shelf architectures such as Diffusion Transformers (Peebles & Xie, 2023) for all of its subcomponents. We demonstrate this leads to significantly improved empirical performance for both the QM9 and GEOM-Drugs datasets. |
| Researcher Affiliation | Academia | Leo Zhang Kianoosh Ashouritaklimi Yee Whye Teh Rob Cornish Department of Statistics, University of Oxford |
| Pseudocode | Yes | Algorithm 1 SYMDIFF training step 1: Sample z0 pdata(z0), t Unif({1, . . . , T}) and ϵ NU(0, I) 2: zt αtz0 + σtϵ 3: Sample R0 from the Haar measure on O(3) and η ν(dη) 4: R R0 fθ(RT 0 zt, η) 5: Take gradient descent step with θ 1 2w(t) ϵ R ϵθ(RT zt) 2 |
| Open Source Code | Yes | Our code is available at: https://github.com/leozhang ML/Sym Diff. |
| Open Datasets | Yes | QM9 (Ramakrishnan et al., 2014) is a common benchmark dataset used for evaluating molecular generation. GEOM-Drugs (Axelrod & Gomez-Bombarelli, 2022) is a larger and more complicated dataset than QM9, containing 430,000 molecules with up to 181 atoms. |
| Dataset Splits | Yes | We used the same train-val-test split of 100K-8K-13K as in Anderson et al. (2019). |
| Hardware Specification | Yes | In fact, when we tried to run the EDM model it resulted in out-of-memory errors on our NVIDIA H100 80GB GPU (Hoogeboom et al. (2022) avoid this by training EDM on 3 NVIDIA RTX A6000 48GB GPUs.) |
| Software Dependencies | No | Both components rely on Diffusion Transformers (Di Ts) (Peebles & Xie, 2023) using the official Py Torch implementation at https: //github.com/facebookresearch/Di T. We also state the hyperparameters that we kept fixed for both our QM9 and GEOM-Drugs experiments. |
| Experiment Setup | Yes | For the optimisation of SYMDIFF models, we followed Peebles & Xie (2023) and used Adam W (Loshchilov & Hutter) with a batch size of 256. We chose a learning rate of 2 10 4 and weight decay of 10 12 for our 31.2M parameter model by searching over a small grid of 3 values for each. To match the same number of steps as in Hoogeboom et al. (2022), we trained our model for 4350 epochs. |