SE(3)-equivariant prediction of molecular wavefunctions and electronic densities
Authors: Oliver Unke, Mihail Bogojeski, Michael Gastegger, Mario Geiger, Tess Smidt, Klaus-Robert Müller
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To assess the ability of Phi SNet to predict molecular wavefunctions and electronic densities (see Fig. 1C), we train it on Kohn-Sham (...) for various non-equilibrium configurations of water, ethanol, malondialdehyde, uracil, and aspirin (...). The results are summarized in Tab. 5 and compared to the current state-of-the-art given by Sch NOrb [33]. |
| Researcher Affiliation | Collaboration | 1 Machine Learning Group, Technische Universität Berlin, 10587 Berlin, Germany 2 DFG Cluster of Excellence Unifying Systems in Catalysis (Uni Sys Cat), Technische Universität Berlin, 10623 Berlin, Germany 3 Institute of Physics, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland 4 Computational Research Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720 5 Center for Advanced Mathematics for Energy Research Applications (CAMERA), Lawrence Berkeley National Laboratory, Berkeley, CA 94720 6 Department of Artificial Intelligence, Korea University, Anam-dong, Seongbuk-gu, Seoul 02841, Korea 7 Max Planck Institute for Informatics, Stuhlsatzenhausweg, 66123 Saarbrücken, Germany 8 BIFOLD Berlin Institute for the Foundations of Learning and Data, Berlin, Germany 9 BASLEARN TU Berlin/BASF Joint Lab for Machine Learning, Technische Universität Berlin, 10587 Berlin, Germany 10 Google Research, Brain Team, Berlin, Germany |
| Pseudocode | No | The paper describes the architecture and its building blocks in detail, and includes diagrams such as Figure 1B, but it does not provide a formal pseudocode block or algorithm listing. |
| Open Source Code | No | The paper discusses general concepts of open-source code and mentions 'open_source_code' as a potential benefit but does not explicitly state that the code for this paper's methodology is open-source or provide a link. |
| Open Datasets | Yes | Datasets for all molecules are taken from [33], with the exception of aspirin, for which geometries were sampled from the MD17 dataset [5] (more details on the datasets, training procedure, and hyperparameter settings can be found in Sections F and G of the supplement). |
| Dataset Splits | No | The paper mentions 'training procedure' in Section 5 and refers to 'Sections F and G of the supplement' for more details, but it does not explicitly state the dataset splits (e.g., train/validation/test percentages or counts) within the main text. |
| Hardware Specification | No | The paper does not specify any particular hardware (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies or version numbers for any libraries or frameworks used. |
| Experiment Setup | No | The paper states that 'more details on the datasets, training procedure, and hyperparameter settings can be found in Sections F and G of the supplement,' but these specific details are not provided in the main text. |