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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Online PCA in Converging Self-consistent Field Equations
Authors: Xihan Li, Xiang Chen, Rasul Tutunov, Haitham Bou Ammar, Lei Wang, Jun Wang
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | demonstrated its high convergence capacity with experiments on both synthesized and real electronic structure scenarios. |
| Researcher Affiliation | Collaboration | 1 University College London 2 Huawei Noah s Ark Lab 3 Huawei R&D U.K. 4 Institute of Physics, Chinese Academy of Sciences |
| Pseudocode | Yes | Algorithm 1 Adaptive Online SCF for solving Equation (8) in electronic structure calculation |
| Open Source Code | No | The paper does not explicitly state that its source code is available or provide a link to a repository for the described methodology. |
| Open Datasets | Yes | In this section, we perform extensive benchmarks on the QM9 dataset [31, 29], a diverse, large dataset to evaluate the capacity of converging Equation (8), the SCF equation in the scenario of electronic structure calculation. |
| Dataset Splits | No | The paper mentions using a sampled subset of the QM9 dataset, but does not provide specific details on how this data is split into training, validation, or test sets for reproduction. |
| Hardware Specification | No | The paper does not provide specific details regarding the hardware (e.g., GPU/CPU models, memory) used to run the experiments. |
| Software Dependencies | No | The paper mentions the use of 'Py SCF [32]', but does not provide a specific version number for this or any other software dependency used in the experiments. |
| Experiment Setup | Yes | Full Online SCF: Algorithm 1 without the adaptive mode switching mechanism. Online SCF is applied throughout the whole iteration process, with a learning rate of 10 2. To avoid the explosion of update interval It Σ when approaching to convergence, we simply set an upper limit of 10,000 for It Σ. Adaptive Online SCF: Algorithm 1 including the adaptive mode switching mechanism. Regular SCF is allow to kick in when the iteration process is close to convergence. Tcut-off and Tcut-off-inc are set to be 100 and 10 respectively. Ttabu is set to be 10. |