From 2ebe79f1f03d85b8306f4a8cfc1fd12c42f84179 Mon Sep 17 00:00:00 2001 From: RaulPPealez Date: Fri, 28 Jun 2024 09:12:24 +0200 Subject: [PATCH] Update docstring --- torchmdnet/datasets/maceoff.py | 10 ++++++++-- 1 file changed, 8 insertions(+), 2 deletions(-) diff --git a/torchmdnet/datasets/maceoff.py b/torchmdnet/datasets/maceoff.py index acb7d2a2..6485dae0 100644 --- a/torchmdnet/datasets/maceoff.py +++ b/torchmdnet/datasets/maceoff.py @@ -20,8 +20,14 @@ class MACEOFF(MemmappedDataset): """ - MACEOFF dataset from MACE-OFF23: Transferable Machine Learning Force Fields for Organic Molecules, Kovacs et.al. https://arxiv.org/abs/2312.15211 - This dataset consists of arounf 100K conformations with 95% of them coming from SPICE and augmented with conformations from QMugs, COMP6 and clusters of water carved out of MD simulations of liquid water. + MACEOFF dataset from MACE-OFF23: Transferable Machine Learning Force Fields for Organic Molecules, Kovacs et.al. https://arxiv.org/abs/2312.15211 + This dataset consists of arounf 100K conformations with 95% of them coming from SPICE and augmented with conformations from QMugs, COMP6 and clusters of water carved out of MD simulations of liquid water. + + From the repository: + The core of the training set is the SPICE dataset. 95% of the data were used for training and validation, and 5% for testing. The MACE-OFF23 model is trained to reproduce the energies and forces computed at the ωB97M-D3(BJ)/def2-TZVPPD level of quantum mechanics, as implemented in the PSI4 software. We have used a subset of SPICE that contains the ten chemical elements H, C, N, O, F, P, S, Cl, Br, and I, and has a neutral formal charge. We have also removed the ion pairs subset. Overall, we used about 85% of the full SPICE dataset. + + Contains energy and force data in units of eV and eV/Angstrom + """ VERSIONS = {