Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
MOFDiff: Coarse-grained Diffusion for Metal-Organic Framework Design
Authors: Xiang Fu, Tian Xie, Andrew Scott Rosen, Tommi S. Jaakkola, Jake Allen Smith
ICLR 2024 | Venue PDF | LLM Run Details | Input Tokens: 28,731 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,813 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We comprehensively evaluate our model s capability to generate valid and novel MOF structures and its effectiveness in designing outstanding MOF materials for carbon capture applications with molecular simulations. |
| Researcher Affiliation | Collaboration | 1MIT CSAIL 2Microsoft Research AI4Science 3Department of Materials Science and Engineering, UC Berkeley 4Materials Science Division, Lawrence Berkeley National Laboratory |
| Pseudocode | Yes | Algorithm 1 Optimize building block orientations for MOF assembly |
| Open Source Code | Yes | Code available at https://github.com/microsoft/MOFDiff. |
| Open Datasets | Yes | We train and evaluate our method on the BW-DB dataset, which contains 304k MOFs with less than 20 building blocks (as defined by the metal-oxo decomposition algorithm) from the 324k MOFs in Boyd et al. 2019. |
| Dataset Splits | Yes | We use 289k MOFs (95%) for training and the rest for validation. |
| Hardware Specification | No | The paper does not specify any particular GPU or CPU models, or other specific hardware configurations used for running experiments. |
| Software Dependencies | Yes | MOFid-v1.1.0, MOFChecker-v0.9.5, egulp-v1.0.0, RASPA2-v2.0.47, LAMMPS-2021-9-29, and Zeo++-v0.3 are used in our experiments. Neural network modules are implemented with Py Torch-v1.11.0 (Paszke et al., 2019), Pyg-v2.0.4 (Fey & Lenssen, 2019), and Lightning-v1.3.8 (Falcon & The Py Torch Lightning team, 2019) with CUDA 11.3. |
| Experiment Setup | Yes | In our experiments, we use 3 rounds: U = 3, with σ = [3, 1.65, 0.3] and k = [30, 16, 1]. We use the Adam optimizer (Kingma & Ba, 2015) to maximize the model-predicted CO2 working capacity for 5,000 steps with a learning rate of 0.0003. All hyperparameters are reported in Table 3. |