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Smith, J. S., Isayev, O., Roitberg, A. E. (2017) ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost. Chemical Science, 8. 3192-3203 doi:10.1039/c6sc05720a

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Reference TypeJournal (article/letter/editorial)
TitleANI-1: an extensible neural network potential with DFT accuracy at force field computational cost
JournalChemical Science
AuthorsSmith, J. S.Author
Isayev, O.Author
Roitberg, A. E.Author
Year2017Volume8
PublisherRoyal Society of Chemistry (RSC)
DOIdoi:10.1039/c6sc05720aSearch in ResearchGate
Generate Citation Formats
Mindat Ref. ID10291257Long-form Identifiermindat:1:5:10291257:1
GUID0
Full ReferenceSmith, J. S., Isayev, O., Roitberg, A. E. (2017) ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost. Chemical Science, 8. 3192-3203 doi:10.1039/c6sc05720a
Plain TextSmith, J. S., Isayev, O., Roitberg, A. E. (2017) ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost. Chemical Science, 8. 3192-3203 doi:10.1039/c6sc05720a
In(n.d.) Chemical Science Vol. 8. Royal Society of Chemistry (RSC)

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Smith, Justin S., Nebgen, Benjamin T., Zubatyuk, Roman, Lubbers, Nicholas, Devereux, Christian, Barros, Kipton, Tretiak, Sergei, Isayev, Olexandr, Roitberg, Adrian E. (2019) Approaching coupled cluster accuracy with a general-purpose neural network potential through transfer learning. Nature Communications, 10. doi:10.1038/s41467-019-10827-4
Smith, Justin S., Isayev, Olexandr, Roitberg, Adrian E. (2017) ANI-1, A data set of 20 million calculated off-equilibrium conformations for organic molecules. Scientific Data, 4. doi:10.1038/sdata.2017.193
Gao, Xiang, Ramezanghorbani, Farhad, Isayev, Olexandr, Smith, Justin S., Roitberg, Adrian E. (2020) TorchANI: A Free and Open Source PyTorch-Based Deep Learning Implementation of the ANI Neural Network Potentials. Journal of Chemical Information and Modeling, 60 (7) 3408-3415 doi:10.1021/acs.jcim.0c00451
Liu, Zhen, Zubatiuk, Tetiana, Roitberg, Adrian, Isayev, Olexandr (2022) Auto3D: Automatic Generation of the Low-Energy 3D Structures with ANI Neural Network Potentials. Journal of Chemical Information and Modeling, 62 (22) 5373-5382 doi:10.1021/acs.jcim.2c00817
Zubatyuk, Roman, Smith, Justin S., Leszczynski, Jerzy, Isayev, Olexandr (2019) Accurate and transferable multitask prediction of chemical properties with an atoms-in-molecules neural network. Science Advances, 5 (8). doi:10.1126/sciadv.aav6490
Smith, Justin S., Zubatyuk, Roman, Nebgen, Benjamin, Lubbers, Nicholas, Barros, Kipton, Roitberg, Adrian E., Isayev, Olexandr, Tretiak, Sergei (2020) The ANI-1ccx and ANI-1x data sets, coupled-cluster and density functional theory properties for molecules. Scientific Data, 7 (1) doi:10.1038/s41597-020-0473-z
Rezaee, Mozafar, Ekrami, Saeid, Hashemianzadeh, Seyed Majid (2024) Comparing ANI-2x, ANI-1ccx neural networks, force field, and DFT methods for predicting conformational potential energy of organic molecules. Scientific Reports, 14 (1) doi:10.1038/s41598-024-62242-5
Smith, Justin S., Nebgen, Ben, Lubbers, Nicholas, Isayev, Olexandr, Roitberg, Adrian E. (2018) Less is more: Sampling chemical space with active learning. The Journal of Chemical Physics, 148 (24). 241733pp. doi:10.1063/1.5023802
Anstine, Dylan M.; Zubatyuk, Roman; Isayev, Olexandr (2025) AIMNet2: a neural network potential to meet your neutral, charged, organic, and elemental-organic needs. Chemical Science, 16 (23). doi:10.1039/d4sc08572h
Guan, Yanfei, Shree Sowndarya, S. V., Gallegos, Liliana C., St. John, Peter C., Paton, Robert S. (2021) Real-time prediction of 1H and 13C chemical shifts with DFT accuracy using a 3D graph neural network. Chemical Science, 12 (36) 12012-12026 doi:10.1039/d1sc03343c
Lahey, Shae-Lynn J., Thien Phuc, Tu Nguyen, Rowley, Christopher N. (2020) Benchmarking Force Field and the ANI Neural Network Potentials for the Torsional Potential Energy Surface of Biaryl Drug Fragments. Journal of Chemical Information and Modeling, 60 (12) 6258-6268 doi:10.1021/acs.jcim.0c00904
 
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