sGDML Symmetric Gradient Domain Machine Learning
subject Articles
Machine Learning of Accurate Energy-conserving Molecular Force Fields
Chmiela, S., Tkatchenko, A., Sauceda, H. E., Poltavsky, I., Schütt, K. T., Müller, K.-R., Science Advances, 3(5), 2017, e1603015.
Towards Exact Molecular Dynamics Simulations with Machine-Learned Force Fields
Chmiela, S., Sauceda, H. E., Müller, K.-R., Tkatchenko, A., Nature Communications, 9(1), 2018, 3887.
sGDML: Constructing Accurate and Data Efficient Molecular Force Fields Using Machine Learning
Chmiela, S., Sauceda, H. E., Poltavsky, I., Müller, K.-R., Tkatchenko, A., Computer Physics Communications, 240, 2019, 38-45.
Molecular Force Fields with Gradient-Domain Machine Learning: Construction and Application to Dynamics of Small Molecules with Coupled Cluster Forces
Sauceda, H. E., Chmiela, S., Poltavsky, I., Müller, K.-R., Tkatchenko, A., The Journal of Chemical Physics, 150, 2019, 114102.
Construction of Machine Learned Force Fields with Quantum Chemical Accuracy: Applications and Chemical Insights
Sauceda, H. E., Chmiela, S., Poltavsky, I., Müller, K.-R., Tkatchenko, A., In: Machine Learning Meets Quantum Physics, Lecture Notes in Physics (Springer), 968, 2020, 277-307.
Ensemble Learning of Coarse-Grained Molecular Dynamics Force Fields with a Kernel Approach
Wang, J., Chmiela, S., Müller, K.-R., Noè, F., Clementi, C., The Journal of Chemical Physics, 152, 2020, 194106.
Dynamical Strengthening of Covalent and Non-Covalent Molecular Interactions by Nuclear Quantum Effects at Finite Temperature
Sauceda, H. E., Vassilev-Galindo, V., Chmiela, S., Müller, K.-R., Tkatchenko, A., Nature Communications, 12(1), 2021, 442.
Molecular Force Fields with Gradient-domain Machine Learning (GDML): Comparison and Synergies with Classical Force Fields
Sauceda, H. E., Gastegger, M., Chmiela, S., Müller, K.-R., Tkatchenko, A., The Journal of Chemical Physics, 153, 2020, 124109.
code Code (latest: v0.4.10)
Replicate our numerical results or reconstruct a force field from your own dataset with a Python implementation of sGDML.
1. Download the aspirin model & dataset.
$ sgdml-get model aspirin
$ sgdml-get dataset aspirin_dft2. Test the model on 10k data points.
$ sgdml test aspirin.npz aspirin_dft.npz 10000file_download Datasets
| Name | Size | Benchmark | Download |
DFT [FHI-aims, light tier 1] |
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| Benzene (Chmiela et al., 2018) | 49 863 | ||
a.k.a. MD17 dataset (Chmiela et al., 2017) |
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| Benzene | 627 983 | ||
| Uracil | 133 770 | ||
| Naphthalene | 326 250 | ||
| Aspirin | 211 762 | ||
| Salicylic acid | 320 231 | ||
| Malonaldehyde | 993 237 | ||
| Ethanol | 555 092 | ||
| Toluene | 442 790 | ||
| NEW Paracetamol | 106 490 | ||
| NEW Azobenzene | 99 999 | ||
CCSD [Psi4, cc-pVDZ] |
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| NEW Aspirin | 1 500 | ||
CCSD(T) [Psi4, cc-pVDZ] |
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| NEW Benzene | 1 500 | ||
| NEW Malonaldehyde | 1 500 | ||
| NEW Toluene | 1 500 | ||
[Psi4, cc-pVTZ] |
|||
| NEW Ethanol | 2 000 | ||