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..
Sampling-free Inference for Ab-Initio Potential Energy Surface Networks
Authors: Nicholas Gao, Stephan Günnemann
ICLR 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In our experimental evaluation, Pla Net accelerates inference by 7 orders of magnitude for larger molecules like ethanol while preserving accuracy. Compared to previous energy surface networks, PESNet++ reduces energy errors by up to 74 %. |
| Researcher Affiliation | Academia | Nicholas Gao, Stephan Günnemann Department of Computer Science & Munich Data Science Institute Technical University of Munich, Germany EMAIL |
| Pseudocode | Yes | Algorithm 1 t + 1th optimization step |
| Open Source Code | Yes | Our source is publicly available 1 licensed under the Hippocratic license (Ehmke, 2019).1https://www.cs.cit.tum.de/daml/pesnet/ |
| Open Datasets | Yes | H4: geometries taken from Pfau et al. (2020). Lithium dimer Li2: 32 evenly distributed distances between 3.5 a0 and 14 a0. Hydrogen chain H10: geometries taken from Motta et al. (2017). Nitrogen dimer N2: geometries taken from Pfau et al. (2020). H2-HF: 64 regular grid points of the N-dimensional energy surface. The boundaries are chosen as: r1, r2 [1.2 a0, 1.8 a0], R [3.0 a0, 8.0 a0], θ1, θ2, φ [0 , 180 ]. Ethanol C2H5OH: 64 evenly distributed torsion angles between 0 and 360 . |
| Dataset Splits | No | The paper refers to 'training' but does not explicitly provide details about training/validation/test dataset splits (e.g., percentages, sample counts, or specific split files) needed for reproduction. |
| Hardware Specification | Yes | We ran all experiments on a machine with 16 AMD EPYC 7742 cores and a single Nvidia A100 GPU. |
| Software Dependencies | No | We implemented Pla Net and PESNet++ on top of the official JAX (Bradbury et al., 2018) implementation of PESNet (Gao & Günnemann, 2022)... |
| Experiment Setup | Yes | Table 4: Default hyperparameters. (includes learning rate, batch size, iterations, WFModel details, Dime Net++ details, Pla Net Optimization hyperparameters) |