Bridging Geometric States via Geometric Diffusion Bridge
Authors: Shengjie Luo, Yixian Xu, Di He, Shuxin Zheng, Tie-Yan Liu, Liwei Wang
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we empirically study the effectiveness of our Geometric Diffusion Bridge on crucial real-world challenges requiring bridging geometric states. In particular, we carefully design several experiments covering different types of data, scales and scenarios, as shown in Table 2. |
| Researcher Affiliation | Collaboration | 1State Key Laboratory of General Artificial Intelligence, School of Intelligence Science and Technology, Peking University 2Microsoft Research AI4Science 3Center for Data Science, Peking University |
| Pseudocode | Yes | Algorithm 3 Training, Algorithm 4 Training with trajectory guidance, Algorithm 5 Sampling, Algorithm 6 Sampling with trajectory guidance |
| Open Source Code | No | The code and model checkpoints will be publicly available after the submission is acceptance. |
| Open Datasets | Yes | QM9 [79] is a medium-scale dataset... Molecule3D [116] is a large-scale dataset... We adopt Open Catalyst 2022 (OC22) dataset [105]... |
| Dataset Splits | Yes | QM9 [79] ... 110k, 10k, and 11k molecules are used for train/valid/test sets respectively; (2) Molecule3D [116] ... its train/valid/test splitting ratio is 6 : 2 : 2. ... The training set consists of 45,890 catalyst-adsorbate complexes. To better evaluate the model s performance, the validation and test sets consider the in-distribution (ID) and out-of-distribution (OOD) settings which use unseen catalysts, containing approximately 2,624 and 2,780 complexes respectively. |
| Hardware Specification | Yes | All models are trained on 16 NVIDIA V100 GPU. All models are trained on 8 NVIDIA A100 GPU. |
| Software Dependencies | No | For training, we use Adam W as the optimizer... we parameterize vθ(Rt, t; R0) by extending a Graph-Transformer based equivariant network [92, 63]... We parameterize vθ(Rt, t; R0) by using Gem Net-OC [34]... |
| Experiment Setup | Yes | For training, we use Adam W as the optimizer, and set the hyper-parameter ϵ to 1e-8 and (β1, β2) to (0.9,0.999). The gradient clip norm is set to 5.0. The peak learning rate is set to 1e-4. The batch size is set to 512. The weight decay is set to 0.0. The model is trained for 500k steps with a 30k-step warm-up stage. ... The noise scale σ is set to 0.5. For inference, we use 10 time steps with the Euler solver [12]. |