Awesome
COmprehensive Machine-learning Potential (COMP6) Benchmark Suite
This repository contains the COMP6 benchmark for evaluating the extensibility of machine-learning based molecular potentials.
If you use the COMP6 benchmark please cite this paper:
Active learning-based (ANI-1x):
Justin S. Smith, Ben Nebgen, Nicholas Lubbers, Olexandr Isayev, Adrian E. Roitberg. Less is more: sampling chemical space with active learning. The Journal of Chemical Physics 148, 241733 (2018), (https://aip.scitation.org/doi/abs/10.1063/1.5023802)
Usage
Please read the README.md in the repository linked below for instructions on how to extract the COMP6 HDF5 (extention *.h5) files. https://github.com/isayev/ANI1_dataset
The following paper contains a description of the file format: https://www.nature.com/articles/sdata2017193
COMP6 Benchmark Results:
These results represent the errors (MAE/RMSE) over the entire benchmark using a single ML potential (column 1). Please read https://aip.scitation.org/doi/abs/10.1063/1.5023802 Section IID for a detailed description of the error metrics.
Please contact Justin S. Smith at jussmith48@gmail.com if you'd like to add your results from the COMP6 benchmark.
Complete COMP6 benchmark results:
| Potential | Energy | Relative Energy | Force |
|---|---|---|---|
| ANI-1x<sup>1</sup> | 1.93/3.37 | 1.85/2.95 | 3.09/5.29 |
| ANI-1<sup>1</sup> | 5.01/16.9 | 3.01/6.97 | 3.70/7.13 |
Units: kcal/mol and kcal/mol/A (errors are NOT per atom) Error key: MAE/RMSE
1) https://aip.scitation.org/doi/abs/10.1063/1.5023802
Related work
ANAKIN-ME ML Potential Method:
Justin S. Smith, Olexandr Isayev, Adrian E. Roitberg. ANI-1: An extensible neural network potential with DFT accuracy at force field computational cost. Chemical Science, 2017, DOI: 10.1039/C6SC05720A
Original ANI-1 data:
Justin S. Smith, Olexandr Isayev, Adrian E. Roitberg. ANI-1, A data set of 20 million calculated off-equilibrium conformations for organic molecules. Scientific Data, 4, Article number: 170193, DOI: 10.1038/sdata.2017.193 https://www.nature.com/articles/sdata2017193
Active learning and transfer learning-based (ANI-1ccx):
Justin S. Smith, Benjamin T. Nebgen, Roman Zubatyuk, Nicholas Lubbers, Christian Devereux, Kipton Barros, Sergei Tretiak, Olexandr Isayev, Adrian Roitberg. Outsmarting Quantum Chemistry Through Transfer Learning. ChemRxiv, 2018, DOI: [https://doi.org/10.26434/chemrxiv.6744440.v1]