AdsorbDiff: Adsorbate Placement via Conditional Denoising Diffusion
Authors: Adeesh Kolluru, John R. Kitchin
ICML 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our findings demonstrate an acceleration of up to 5x or 3.5x improvement in accuracy compared to the previous best approach. Given the novelty of this framework and application, we provide insights into the impact of pre-training, model architectures, and conduct extensive experiments to underscore the significance of this approach. |
| Researcher Affiliation | Academia | 1Department of Chemical Engineering, Carnegie Mellon University. Correspondence to: Adeesh Kolluru <akolluru@andrew.cmu.edu>, John R. Kitchin <jkitchin@andrew.cmu.edu>. |
| Pseudocode | Yes | For the initial coordinates of adsorbate, we select a random point on the slab. This point is considered as the center-of-mass of the adsorbate in fractional coordinates. We then convert from fractional coordinates to real coordinates and perform a reverse diffusion process to get to the lowest energy site (as shown in Algorithm 1). |
| Open Source Code | Yes | The code is open-sourced with MIT License1. 1https://github.com/Adeesh Kolluru/ Adsorb Diff |
| Open Datasets | Yes | We utilize two publicly available datasets for this work OC20-Dense (Lan et al., 2023) and OC20 (Chanussot et al., 2021). |
| Dataset Splits | Yes | We split the ID data into 80/20 ratios for training the diffusion model and validating the sampling process. |
| Hardware Specification | Yes | We perform all of our training on 2 48GB A6000 GPUs. |
| Software Dependencies | No | The paper mentions using specific algorithms like L-BFGS and models like Equiformer V2, Gem Net-OC, and Pai NN, but it does not provide specific version numbers for software libraries or dependencies like Python, PyTorch, etc. |
| Experiment Setup | Yes | The hyperparameters of L-BFGS are mentioned in Table 4. We set a max step size for sampling and stop early if the movement of atoms converges. These hyperparameters are shown in Table 5. Maximum learning rate was kept to be 4.e-4 and 1.e-4 for pretraining and finetuning runs respectively utilizing Adam W optimizer. For each model architecture, maximum batch size was utilized that could fit on the GPU. |