Neural Message Passing for Quantum Chemistry
Authors: Justin Gilmer, Samuel S. Schoenholz, Patrick F. Riley, Oriol Vinyals, George E. Dahl
ICML 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Using MPNNs we demonstrate state of the art results on an important molecular property prediction benchmark; these results are strong enough that we believe future work should focus on datasets with larger molecules or more accurate ground truth labels. In table 2 we compare the performance of our best MPNN variant (denoted with enn-s2s) and the corresponding ensemble (denoted with enn-s2s-ens5) with the previous state of the art on this dataset as reported in Faber et al. (2017). |
| Researcher Affiliation | Industry | 1Google Brain 2Google 3Google Deep Mind. Correspondence to: Justin Gilmer <gilmer@google.com>, George E. Dahl <gdahl@google.com>. |
| Pseudocode | No | The paper describes mathematical functions and steps but does not include any explicitly labeled 'Pseudocode' or 'Algorithm' blocks. |
| Open Source Code | No | The paper does not provide a statement about releasing open-source code or a link to a code repository for the described methodology. |
| Open Datasets | Yes | To investigate the success of MPNNs on predicting chemical properties, we use the publicly available QM9 dataset (Ramakrishnan et al., 2014). |
| Dataset Splits | Yes | The QM-9 dataset has 130462 molecules in it. We randomly chose 10000 samples for validation, 10000 samples for testing, and used the rest for training. |
| Hardware Specification | No | The paper mentions 'Xeon E5-2660 (2.2 GHz)' in the context of DFT calculations (a baseline), but does not specify the hardware used for training or running the MPNN experiments described in the paper. |
| Software Dependencies | No | The paper mentions optimizers (ADAM) and units (GRU) but does not list specific software dependencies with version numbers (e.g., PyTorch, TensorFlow, Python version) used for the experiments. |
| Experiment Setup | Yes | Each model and target combination was trained using a uniform random hyper parameter search with 50 trials. T was constrained to be in the range 3 T 8 (in practice, any T 3 works). The number of set2set computations M was chosen from the range 1 M 12. All models were trained using SGD with the ADAM optimizer (Kingma & Ba (2014)), with batch size 20 for 3 million steps ( 540 epochs). The initial learning rate was chosen uniformly between 1e 5 and 5e 4. We used a linear learning rate decay that began between 10% and 90% of the way through training and the initial learning rate l decayed to a final learning rate l F, using a decay factor F in the range [.01, 1]. |