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 [1].
Variational Monte Carlo on a Budget — Fine-tuning pre-trained Neural Wavefunctions
Authors: Michael Scherbela, Leon Gerard, Philipp Grohs
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We extensively evaluate the accuracy, scalability and limitations of our base model on a wide variety of test systems. |
| Researcher Affiliation | Academia | Michael Scherbela University of Vienna EMAIL Leon Gerard* University of Vienna EMAIL Philipp Grohs University of Vienna EMAIL |
| Pseudocode | No | No pseudocode or algorithm blocks were explicitly labeled or formatted as such in the paper. |
| Open Source Code | Yes | All code, configuration files, geometries, datasets and obtained energies are available on Git Hub under https://github.com/mdsunivie/deeperwin. |
| Open Datasets | Yes | A chemically diverse dataset with up to 100 molecules based on QM7-X [26]... To train our pre-trained wavefunctions we use two datasets, consisting of 18 and 98 disparate molecules... For the large scale experiment we used a stratified random sample of 250 molecules from the QM7 dataset [32]. |
| Dataset Splits | No | The paper does not explicitly describe a separate validation dataset split with percentages or sample counts. |
| Hardware Specification | Yes | Overall we used 5k GPUhs (A100) for development and training of our base models, and another 5k GPUhs (A40) on evaluations and finetuning. |
| Software Dependencies | No | The paper mentions various software components such as 'py SCF [35]', 'e3nn library [36]', 'Adam optimizer [37]', 'RDKit [38]', 'ORCA [39]', 'Fermi Net codebase [40]', and 'KFAC [30] and their Python implementation [41]', but does not specify their exact version numbers for reproducibility. |
| Experiment Setup | Yes | A detailed description of the hyperparameter used in this work can be found below (cf. Tab. 2). Table 2: Hyperparameter settings used in this work. |