Machine-Learning Forcefields

Cambridge Gabor Csanyi

MACE

• https://github.com/ACEsuit/mace
• https://mace-docs.readthedocs.io/_/downloads/en/latest/pdf/
• Batatia, I.; Batzner, S.; Kovács, D. P.; Musaelian, A.; Simm, G. N. C.; Drautz, R.; Ortner, C.; Kozinsky, B.; Csányi, G. The Design Space of E(3)-Equivariant Atom-Centered Interatomic Potentials,’ Nat. Mach. Intell. 7 56-67 (2025).
• Batatia, I.; Kovacs, D. P.; Simm, G.; Ortner, C.; Csanyi, G. MACE: Higher Order Equivariant Message Passing Neural Networks for Fast and Accurate Force Fields,’ Adv. Neural Inf. Process. Syst. 35 11423–11436 (2022).

QUIP

• https://github.com/libAtoms/QUIP
• Csányi, G.; Winfield, S.; Kermode, J. R.; De Vita, A.; Comisso, A.; Bernstein, N.; Payne, M. C. Expressive Programming for Computational Physics in Fortran 95+,’ IoP Comput. Phys. Newsletter Spring 2007 (2007),
• Bartók, A. P., Payne, M. C., Kondor, R., & Csányi, G. Gaussian approximation potentials: The accuracy of quantum mechanics, without the electrons,’ Phys. Rev. Lett. 104 136403 (2010).

Miscellaneous

• Deringer, V. L., & Csányi, G. Gaussian Approximation Potentials: The Accuracy of Quantum Mechanics, without the Electrons,’ Phys. Rev. Lett. 104 136403 (2010).
• Bartók, A. P., Kondor, R., & Csányi, G. On representing chemical environments,’ Phys. Rev. B 87 219902 (2013).

Harvard Boris Kozinsky

FLARE

• https://github.com/mir-group/flare
• Vandermause, J., Torrisi, S.B., Batzner, S. et al. On-the-fly active learning of interpretable Bayesian force fields for atomistic rare events. npj Comput Mater 6, 20 (2020).
• Vandermause, J., Xie, Y., Lim, J.S. et al. Active learning of reactive Bayesian force fields applied to heterogeneous catalysis dynamics of H/Pt. Nat Commun 13, 5183 (2022).

NequIP

• https://github.com/mir-group/nequip
• Batzner, S., Musaelian, A., Sun, L. et al. E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials. Nat Commun 13, 2453 (2022).

Allegro

• https://github.com/mir-group/allegro
• Musaelian, A., Batzner, S., Johansson, A. et al. Learning local equivariant representations for large-scale atomistic dynamics. Nat Commun 14, 579 (2023).

M3GNet

• https://github.com/materialsvirtuallab/matgl
• https://github.com/materialsvirtuallab/maml
• Chen, C., Ong, S.P. A universal graph deep learning interatomic potential for the periodic table. Nat Comput Sci 2, 718–728 (2022).

DeePMD-kit

• https://github.com/deepmodeling/deepmd-kit
• Wang, H.; Zhang, L.; Han, J.; E, W. DeePMD-kit: A Deep Learning Package for Many-Body Potential Energy Representation and Molecular Dynamics, Comput. Phys. Commun. 228 178-184 (2018).
• Zeng, J.; Zhang, D.; Lu, D.; Mo, P.; Li, Z.; Chen, Y.; Rynik, M.; Huang, L.; Li, Z.; Shi, S.; Wang, Y.; Ye, H.; Tuo, P.; Yang, J.; Ding, Y.; Li, Y.; Tisi, D.; Zeng, Q.; Bao, H.; Xia, Y., et al. DeePMD-kit v2: A Software Package for Deep Potential Models, J. Chem. Phys. 159 054801 (2023).
• Zeng, J.; Zhang, D.; Peng, A.; Zhang, X.; He, S.; Wang, Y.; Liu, X.; Bi, H.; Li, Y.; Cai, C.; Zhang, C.; Du, Y.; Zhu, J.-X.; Mo, P.; Huang, Z.; Zeng, Q.; Shi, S.; Qin, X.; Yu, Z.; Luo, C., et al. DeePMD-kit v3: A Multiple-Backend Framework for Machine Learning Potentials. J. Chem. Theory Comput. 21, 4375–4385 (2025).
• https://github.com/deepmodeling/dpgen
• https://github.com/deepmodeling/dpgen2
• Zhang, Y.; Wang, H.; Chen, W.; Zeng, J.; Zhang, L.; Wang, H.; E, W. DP-GEN: A concurrent learning platform for the generation of reliable deep learning based potential energy models,’ Comput. Phys. Commun. 253, 107206 (2020).

Miscellaneous

Behler-Parinello

• Physical review letters, 98(14):146401, 2007
• Physical Chemistry Chemical Physics, 13(40):17930–17955, 2011

ANI-1

• Chemical science, 8(4):3192–3203, 2017

SNAP

• Journal of Computational Physics, 285:316–330, 2015
• Journal of chemical physics, 148(24):241721, 2018

MTP

• Multiscale Modeling & Simulation, 14(3):1153–1173, 2016

ACE

• Physical Review B, 99(1):014104, 2019.

DeePMD Notes

1. A good starting point is to run the water examples. There are JSON input files available for various descriptors.

📦 https://github.com/deepmodeling/deepmd-kit/tree/master/examples/water

se_e2_a should be one of the standard descriptors to try. It is short for the Deep Potential Smooth Edition (DeepPot-SE), which is constructed from the angular and radial features of atomic configurations. Later model frameworks, such as dpa-1, 2, 3, stand for Deep Potential models with an attention mechanism using the descriptor se_atten.

2. sel is an important parameter, which means the expected maximum number of the neighbors of a type of atom. DeePMD-kit will allocate memory according to sel.

📖 https://docs.deepmodeling.com/projects/deepmd/en/stable/model/sel.html

dp --pt neighbor-stat -s data -r 6.0 -t O H

3. There is no available option to save the best model from the validation performance currently, Related posts as following:

💬 How to save the best model on validation data during model training? (Unanswered)

💬 Save model with best validation Error

There is a training parameter max_ckpt_keep can change the latest maximum number of the checkpoints to keep. We can adjust this larger and check the learning curve output data to choose the best model from validation performance metrics.

4. dpdata can be used to perform the conversion and generate the npy data, which is the recommended format in DeepMD, with the following command:

dpdata OUTCAR -i vasp/outcar -o deepmd/npy -O deepmd_data

If the training input is from single point energy calculations, there is one post from the discussion forum, but it seems they did not give a clear reply.

💬 DeepMD training model with Single Point energy calculations

We can simply concatenate the OUTCAR files (e.g., OUTCAR_1, OUTCAR_2, …) into a single OUTCAR and run this command to produce a dataset as if dealing with the AIMD OUTCAR.

However, it assumes that all images have the same atom types and counts (for example, AIMD OUTCAR outputs). This may not be valid for cases where the number and order of elements vary across different images. In that case, we should convert one-by-one with dpdata on the single point energy OUTCAR. The following is a related to-do enhancement from the deepmd-kit:

💬 Support batched inference with different natoms

5. There is a provided script to plot the learning curve log data from their documentation.

📖 https://tutorials.deepmodeling.com/en/latest/Tutorials/DeePMD-kit/learnDoc/Handson-Tutorial%28v2.0.3%29.html#train-process

6. There is no limit on the radius cutoff to be less than half box size, which is explained as following:

📖 https://docs.deepmodeling.com/projects/deepmd/en/stable/troubleshooting/howtoset_rcut.html