ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost
Open accessAbstract
We demonstrate how a deep neural network (NN) trained on a data set of quantum mechanical (QM) DFT calculated energies can learn an accurate and transferable atomistic potential for organic molecules containing H, C, N, and O atoms.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
ANI-1 predicted DFT energies of test set molecules with up to 10 heavy atoms with RMSE values below 0.57 kcal/mol.
“It predicted DFT energies of the test set molecules with up to 10 heavy atoms very well, with the resulting RMSE values below 0.57 kcal/mol.”
From QSAR without borders (2020) · cites this paper as [189]
ANI-1 is the first NNP designed for organic molecules and is applicable to molecular systems well outside its training set.
“Recently, Smith et al. introduced the first NNP designed for organic molecules, ANI-1.189 It is applicable to molecular systems well outside its training set.”
From QSAR without borders (2020) · cites this paper as [189]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946 · see citing works
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126 · see citing works
(2014) · Cited by 33,419 · see citing works
Multilayer feedforward networks are universal approximators
Hornik, K., Stinchcombe, M., & White, H. (1989) · Neural Networks · doi:10.1016/0893-6080(89)90020-8 · Cited by 21,728 · see citing works
Deep learning in neural networks: an overview
Schmidhuber, J. (2015) · Neural Networks · doi:10.1016/j.neunet.2014.09.003 · Cited by 18,570 · see citing works
Dewar, M. J., Zoebisch, E., Healy, E. F., & Stewart, J. (1985) · Journal of the American Chemical Society · doi:10.1021/ja00299a024 · Cited by 11,560 · see citing works
ff14SB: Improving the Accuracy of Protein Side Chain and Backbone Parameters from ff99SB
Maier, J., Martinez, C., Kasavajhala, K., Wickstrom, L., Hauser, K., & Simmerling, C. L. (2015) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.5b00255 · Cited by 10,971 · see citing works
Comparison of multiple Amber force fields and development of improved protein backbone parameters
Horn̆ák, V., Abel, R., Okur, A., Strockbine, B., Roitberg, A. E., & Simmerling, C. L. (2006) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.21123 · Cited by 6,903 · see citing works
Self-citationReaxFF: A Reactive Force Field for Hydrocarbons
van Duin, A. V., Dasgupta, S., Lorant, F., & Goddard, W. (2001) · The Journal of Physical Chemistry A · doi:10.1021/jp004368u · Cited by 6,104 · see citing works
Sun, H. (1998) · The Journal of Physical Chemistry B · doi:10.1021/jp980939v · Cited by 6,016 · see citing works
Merck molecular force field. I. Basis, form, scope, parameterization, and performance of MMFF94
Halgren, T. A. (1996) · Journal of Computational Chemistry · doi:10.1002/(sici)1096-987x(199604)17:5/6 · Cited by 5,321 · see citing works
Generalized Neural-Network Representation of High-Dimensional Potential-Energy Surfaces
Behler, J., & Parrinello, M. (2007) · Physical Review Letters · doi:10.1103/physrevlett.98.146401 · Cited by 5,010 · see citing works
Systematic optimization of long-range corrected hybrid density functionals
Chai, J., & Head‐Gordon, M. (2008) · The Journal of Chemical Physics · doi:10.1063/1.2834918 · Cited by 4,063 · see citing works
On representing chemical environments
Bartók, A. P., Kondor, R., & Csányi, G. (2013) · Physical Review B · doi:10.1103/physrevb.87.184115 · Cited by 2,572 · see citing works
Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning
Rupp, M., Tkatchenko, A., Müller, K.-R., & von Lilienfeld, O. A. (2012) · Physical Review Letters · doi:10.1103/physrevlett.108.058301 · Cited by 2,312 · see citing works
Quantum chemistry structures and properties of 134 kilo molecules
Ramakrishnan, R., Dral, P. O., Rupp, M., & von Lilienfeld, O. A. (2014) · Scientific Data · doi:10.1038/sdata.2014.22 · Cited by 1,966 · see citing works
Fragment molecular orbital method: an approximate computational method for large molecules
Kitaura, K., Ikeo, E., Asada, T., et al. (1999) · Chemical Physics Letters · doi:10.1016/s0009-2614(99)00874-x · Cited by 1,439 · see citing works
Regularization Algorithms for Learning That Are Equivalent to Multilayer Networks
Poggio, T., & Girosi, F. (1990) · Science · doi:10.1126/science.247.4945.978 · Cited by 1,024 · see citing works
Constructing high‐dimensional neural network potentials: A tutorial review
Behler, J. (2015) · International Journal of Quantum Chemistry · doi:10.1002/qua.24890 · Cited by 864 · see citing works
Hansen, K., Biegler, F., Ramakrishnan, R., Pronobis, W., von Lilienfeld, O. A., Müller, K.-R., & Tkatchenko, A. (2015) · The Journal of Physical Chemistry Letters · doi:10.1021/acs.jpclett.5b00831 · Cited by 815 · see citing works
Neural network potential-energy surfaces in chemistry: a tool for large-scale simulations
Behler, J. (2011) · Physical Chemistry Chemical Physics · doi:10.1039/c1cp21668f · Cited by 781 · see citing works
Universal fragment descriptors for predicting properties of inorganic crystals
Isayev, O., Oses, C., Toher, C., Gossett, E., Stefano Curtarolo, A., & Tropsha, A. (2017) · Nature Communications · doi:10.1038/ncomms15679 · Cited by 638 · see citing works
Self-citationAmphetamine, past and present – a pharmacological and clinical perspective
Heal, D. J., Smith, S. L., Gosden, J., & Nutt, D. (2013) · Journal of Psychopharmacology · doi:10.1177/0269881113482532 · Cited by 622 · see citing works
How van der Waals interactions determine the unique properties of water
Morawietz, T., Singraber, A., Dellago, C., & Behler, J. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1602375113 · Cited by 487 · see citing works
Fink, T., & Reymond, J. (2007) · Journal of Chemical Information and Modeling · doi:10.1021/ci600423u · Cited by 457 · see citing works
Machine Learning Energies of 2 Million Elpasolite ( A B C 2 D 6 ) Crystals
Faber, F. A., Lindmaa, A., von Lilienfeld, O. A., & Armiento, R. (2016) · Physical Review Letters · doi:10.1103/physrevlett.117.135502 · Cited by 404 · see citing works
Jiang, B., Li, J., & Guo, H. (2016) · International Reviews in Physical Chemistry · doi:10.1080/0144235x.2016.1200347 · Cited by 401 · see citing works
Exploring chemistry with the fragment molecular orbital method
Fedorov, D. G., Nagata, T., & Kitaura, K. (2012) · Physical Chemistry Chemical Physics · doi:10.1039/c2cp23784a · Cited by 376 · see citing works
Virtual Exploration of the Small‐Molecule Chemical Universe below 160 Daltons
Fink, T., Bruggesser, H., & Reymond, J. (2005) · Angewandte Chemie International Edition · doi:10.1002/anie.200462457 · Cited by 356 · see citing works
The SCC-DFTB method and its application to biological systems
Elstner, M. (2006) · Theoretical Chemistry Accounts · doi:10.1007/s00214-005-0066-0 · Cited by 349 · see citing works
Potential Energy Surfaces Fitted by Artificial Neural Networks
Handley, C. M., & Popelier, P. L. A. (2010) · The Journal of Physical Chemistry A · doi:10.1021/jp9105585 · Cited by 328 · see citing works
Application of the PM6 method to modeling proteins
Stewart, J. J. P. (2008) · Journal of Molecular Modeling · doi:10.1007/s00894-008-0420-y · Cited by 324 · see citing works
Accuracy and transferability of Gaussian approximation potential models for tungsten
Szlachta, W. J., Bartók, A. P., & Cśanyi, G. (2014) · Physical Review B · doi:10.1103/physrevb.90.104108 · Cited by 318 · see citing works
Manzhos, S., & Carrington, T. (2006) · The Journal of Chemical Physics · doi:10.1063/1.2336223 · Cited by 257 · see citing works
Machine Learning for Quantum Mechanical Properties of Atoms in Molecules
Rupp, M., Ramakrishnan, R., & von Lilienfeld, O. A. (2015) · The Journal of Physical Chemistry Letters · doi:10.1021/acs.jpclett.5b01456 · Cited by 219 · see citing works
von Lilienfeld, O. A., Ramakrishnan, R., Rupp, M., & Knoll, A. M. (2015) · International Journal of Quantum Chemistry · doi:10.1002/qua.24912 · Cited by 218 · see citing works
The history and development of the fentanyl series
Stanley, T. H. (1992) · Journal of Pain and Symptom Management · doi:10.1016/0885-3924(92)90047-l · Cited by 214 · see citing works
Manzhos, S., Dawes, R., & Carrington, T. (2014) · International Journal of Quantum Chemistry · doi:10.1002/qua.24795 · Cited by 204 · see citing works
Raff, L. M., Malshe, M., Hagan, M., Doughan, D. I., Rockley, M. G., & Komanduri, R. (2005) · The Journal of Chemical Physics · doi:10.1063/1.1850458 · Cited by 155 · see citing works
Machine learning exciton dynamics
Häse, F., Valleau, S., Pyzer-Knapp, E., & Aspuru‐Guzik, A. (2016) · Chemical Science · doi:10.1039/c5sc04786b · Cited by 135 · see citing works
Reviews in Computational Chemistry
(2007) · Reviews in computational chemistry · doi:10.1002/series6143 · Cited by 132 · see citing works
Perspectives on Semiempirical Molecular Orbital Theory
Thiel, W. (1996) · Advances in chemical physics · doi:10.1002/9780470141526.ch10 · Cited by 106 · see citing works
Gastegger, M., Kauffmann, C., Behler, J., & Marquetand, P. (2016) · The Journal of Chemical Physics · doi:10.1063/1.4950815 · Cited by 63 · see citing works
Kolb, B., Zhao, B., Li, J., Jiang, B., & Guo, H. (2016) · The Journal of Chemical Physics · doi:10.1063/1.4953560 · Cited by 60 · see citing works
Ghosh, S., & Hammes‐Schiffer, S. (2014) · The Journal of Physical Chemistry Letters · doi:10.1021/jz5023784 · Cited by 28 · see citing works
Ho, T. H., Pham‐Tran, N., Kawazoe, Y., & Le, H. M. (2016) · The Journal of Physical Chemistry A · doi:10.1021/acs.jpca.5b09497 · Cited by 13 · see citing works
Space-Filling Curves as a Novel Crystal Structure Representation for Machine Learning ModelsPreprint
Jasrasaria, D., Pyzer‐Knapp, E. O., Rappoport, D., et al. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1608.05747 · Cited by 5 · see citing works
Flagged first, then by citations
Research works printed but not linked: 4
- Schütt K. T., Arbabzadah F., Chmiela S., Müller K. R. and Tkatchenko A., 2016, arXiv.org:1609.08259.
- Rupp M. Ramakrishnan R. von Lilienfeld O. A. J. Phys. Chem. Lett. 2015 6 1 5 26113956 PMC4476293
- Rupp M., Ramakrishnan R. and von Lilienfeld O. A., arXiv:1505.00350, 2015, 6, 1 5. · arXiv
- Amaral T., Silva L. M., Alexandre L. A., Kandaswamy C., Santos J. M. and De J. M. S., in Proceedings - 2013 12th Mexican International Conference on Artificial Intelligence, MICAI 2013, IEEE, 2013, pp. 114–120.
Cited by
Sorted by citations
Machine learning for molecular and materials science
Butler, K. T., Davies, D. W., Cartwright, H., Isayev, O., & Walsh, A. (2018) · Nature · doi:10.1038/s41586-018-0337-2 · Cited by 4,968 · see citing works
Self-citationSchNet – A deep learning architecture for molecules and materials
Schütt, K. T., Sauceda, H. E., Kindermans, P.-J., Tkatchenko, A., & Müller, K.-R. (2018) · The Journal of Chemical Physics · doi:10.1063/1.5019779 · Cited by 2,217 · see citing works
Analyzing Learned Molecular Representations for Property Prediction
Yang, K., Swanson, K., Jin, W., Coley, C., Eiden, P., Gao, H., Guzman-Perez, A., Hopper, T., Kelley, B., Mathea, M., et al. (2019) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.9b00237 · Cited by 1,923 · see citing works
Scientific discovery in the age of artificial intelligence.
Wang, H., Fu, T., Du, Y., Gao, W., Huang, K., Liu, Z., Chandak, P., Liu, S., Van Katwyk, P., Deac, A., et al. (2023) · Nature · doi:10.1038/s41586-023-06221-2 · Cited by 1,823 · see citing works
“To improve the accuracy of molecular simulations, an AI-based neural potential that fits expensive yet accurate quantum-mechanical data has been developed to replace traditional force fields 16,168 .”
MethodRecent advances and applications of deep learning methods in materials science
Choudhary, K., DeCost, B., Chen, C., Jain, A., Tavazza, F., Cohn, R., Park, C. W., Choudhary, A., Agrawal, A., Billinge, S. J. L., et al. (2022) · npj Computational Materials · doi:10.1038/s41524-022-00734-6 · Cited by 1,172 · see citing works
“com/isayev/ANI1 dataset [80] MD17 1mil http://quantum-machine.”
MethodMachine learning in materials science
Wei, J., Chu, X., Sun, X., Xu, K., Deng, H., Chen, J., Wei, Z., & Lei, M. (2019) · InfoMat · doi:10.1002/inf2.12028 · Cited by 1,069 · see citing works
Machine Learning for Molecular Simulation
Noé, F., Tkatchenko, A., Müller, K.-R., & Clementi, C. (2020) · Annual Review of Physical Chemistry · doi:10.1146/annurev-physchem-042018-052331 · Cited by 956 · see citing works
Muratov, E. N., Bajorath, J., Sheridan, R. P., Tetko, I. V., Filimonov, D., Poroikov, V., Oprea, T. I., Baskin, I. I., Varnek, A., Roitberg, A., et al. (2020) · Chemical Society Reviews · doi:10.1039/d0cs00098a · Cited by 913 · see citing works
Self-citationFrom DFT to machine learning: recent approaches to materials science–a review
Schleder, G. R., Padilha, A. C. M., Acosta, C. M., Costa, M., & Fazzio, A. (2019) · Journal of Physics: Materials · doi:10.1088/2515-7639/ab084b · Cited by 852 · see citing works
Data‐Driven Materials Science: Status, Challenges, and Perspectives
Himanen, L., Geurts, A., Foster, A. S., & Rinke, P. (2019) · Advanced Science · doi:10.1002/advs.201900808 · Cited by 808 · see citing works
Machine learning unifies the modeling of materials and molecules
Bartók, A. P., De, S., Poelking, C., Bernstein, N., Kermode, J. R., Csányi, G., & Ceriotti, M. (2017) · Science Advances · doi:10.1126/sciadv.1701816 · Cited by 764 · see citing works
Prediction Errors of Molecular Machine Learning Models Lower than Hybrid DFT Error
Faber, F. A., Hutchison, L., Huang, B., Gilmer, J., Schoenholz, S. S., Dahl, G. E., Vinyals, O., Kearnes, S., Riley, P. F., & von Lilienfeld, O. A. (2017) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.7b00577 · Cited by 651 · see citing works
A Critical Review of Machine Learning of Energy Materials
Chen, C., Zuo, Y., Ye, W., Li, X., Deng, Z., & Ong, S. P. (2020) · Advanced Energy Materials · doi:10.1002/aenm.201903242 · Cited by 609 · see citing works
Large-scale comparison of machine learning methods for drug target prediction on ChEMBL
Mayr, A., Klambauer, G., Unterthiner, T., Steijaert, M., Wegner, J. K., Ceulemans, H., Clevert, D.-A., & Hochreiter, S. (2018) · Chemical Science · doi:10.1039/c8sc00148k · Cited by 584 · see citing works
Mater, A. C., & Coote, M. L. (2019) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.9b00266 · Cited by 565 · see citing works
Machine learning molecular dynamics for the simulation of infrared spectra
Gastegger, M., Behler, J., & Marquetand, P. (2017) · Chemical Science · doi:10.1039/c7sc02267k · Cited by 556 · see citing works
Pyzer-Knapp, E. O., Pitera, J. W., Staar, P. W. J., Takeda, S., Laino, T., Sanders, D. P., Sexton, J., Smith, J. R., & Curioni, A. (2022) · npj Computational Materials · doi:10.1038/s41524-022-00765-z · Cited by 465 · see citing works
Self-citationImbalzano, G., Anelli, A., Giofré, D., Klees, S., Behler, J., & Ceriotti, M. (2018) · The Journal of Chemical Physics · doi:10.1063/1.5024611 · Cited by 330 · see citing works
Machine learning approaches for analyzing and enhancing molecular dynamics simulations
Wang, Y., Lamim Ribeiro, J. M., & Tiwary, P. (2020) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2019.12.016 · Cited by 303 · see citing works
Janet, J. P., & Kulik, H. J. (2017) · The Journal of Physical Chemistry A · doi:10.1021/acs.jpca.7b08750 · Cited by 289 · see citing works
A quantitative uncertainty metric controls error in neural network-driven chemical discovery
Janet, J. P., Duan, C., Yang, T., Nandy, A., & Kulik, H. J. (2019) · Chemical Science · doi:10.1039/c9sc02298h · Cited by 243 · see citing works
Deep Learning in Protein Structural Modeling and Design
Gao, W., Mahajan, S. P., Sulam, J., & Gray, J. J. (2020) · Patterns · doi:10.1016/j.patter.2020.100142 · Cited by 227 · see citing works
The prospects of quantum computing in computational molecular biology
Outeiral, C., Strahm, M., Shi, J., Morris, G. M., Benjamin, S. C., & Deane, C. M. (2021) · WIREs Computational Molecular Science · doi:10.1002/wcms.1481 · Cited by 232 · see citing works
A critical overview of computational approaches employed for COVID-19 drug discovery
Muratov, E. N., Amaro, R., Andrade, C. H., Brown, N., Ekins, S., Fourches, D., Isayev, O., Kozakov, D., Medina-Franco, J. L., Merz, K. M., et al. (2021) · Chemical Society Reviews · doi:10.1039/d0cs01065k · Cited by 206 · see citing works
Self-citationPredicting electronic structure properties of transition metal complexes with neural networks
Janet, J. P., & Kulik, H. J. (2017) · Chemical Science · doi:10.1039/c7sc01247k · Cited by 199 · see citing works
Machine-learned interatomic potentials by active learning: amorphous and liquid hafnium dioxide
Sivaraman, G., Krishnamoorthy, A. N., Baur, M., Holm, C., Stan, M., Csányi, G., Benmore, C., & Vázquez-Mayagoitia, Á. (2020) · npj Computational Materials · doi:10.1038/s41524-020-00367-7 · Cited by 194 · see citing works
Atom-density representations for machine learning
Willatt, M. J., Musil, F., & Ceriotti, M. (2019) · The Journal of Chemical Physics · doi:10.1063/1.5090481 · Cited by 179 · see citing works
Unsupervised machine learning in atomistic simulations, between predictions and understanding
Ceriotti, M. (2019) · The Journal of Chemical Physics · doi:10.1063/1.5091842 · Cited by 173 · see citing works
Strategies and Software for Machine Learning Accelerated Discovery in Transition Metal Chemistry
Nandy, A., Duan, C., Janet, J. P., Gugler, S., & Kulik, H. J. (2018) · Industrial & Engineering Chemistry Research · doi:10.1021/acs.iecr.8b04015 · Cited by 161 · see citing works
DPA-2: a large atomic model as a multi-task learner
Zhang, D., Liu, X., Zhang, X., Zhang, C., Cai, C., Bi, H., Du, Y., Qin, X., Peng, A., Huang, J., et al. (2023) · arXiv · doi:10.48550/arxiv.2312.15492 · Cited by 155
Willatt, M. J., Musil, F., & Ceriotti, M. (2018) · Physical Chemistry Chemical Physics · doi:10.1039/c8cp05921g · Cited by 141 · see citing works
Intrinsic Bond Energies from a Bonds-in-Molecules Neural Network
Yao, K., Herr, J. E., Brown, S. N., & Parkhill, J. (2017) · The Journal of Physical Chemistry Letters · doi:10.1021/acs.jpclett.7b01072 · Cited by 126 · see citing works
Application of machine learning for advanced material prediction and design
Chan, C. H., Sun, M., & Huang, B. (2022) · EcoMat · doi:10.1002/eom2.12194 · Cited by 123 · see citing works
Janet, J. P., Liu, F., Nandy, A., Duan, C., Yang, T., Lin, S., & Kulik, H. J. (2019) · Inorganic Chemistry · doi:10.1021/acs.inorgchem.9b00109 · Cited by 114 · see citing works
The many-body expansion combined with neural networks
Yao, K., Herr, J. E., & Parkhill, J. (2017) · The Journal of Chemical Physics · doi:10.1063/1.4973380 · Cited by 120 · see citing works
AlQuraishi, M., & Sorger, P. K. (2021) · Nature Methods · doi:10.1038/s41592-021-01283-4 · Cited by 96 · see citing works
Duan, C., Janet, J. P., Liu, F., Nandy, A., & Kulik, H. J. (2019) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.9b00057 · Cited by 87 · see citing works
Generalized Density-Functional Tight-Binding Repulsive Potentials from Unsupervised Machine Learning
Kranz, J. J., Kubillus, M., Ramakrishnan, R., von Lilienfeld, O. A., & Elstner, M. (2018) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.7b00933 · Cited by 63 · see citing works
Saikin, S. K., Kreisbeck, C., Sheberla, D., Becker, J. S., & Aspuru-Guzik, A. (2019) · Expert Opinion on Drug Discovery · doi:10.1080/17460441.2019.1546690 · Cited by 63 · see citing works
Equivariant Graph Neural Networks for Toxicity Prediction
Cremer, J., Medrano Sandonas, L., Tkatchenko, A., Clevert, D.-A., & De Fabritiis, G. (2023) · Chemical Research in Toxicology · doi:10.1021/acs.chemrestox.3c00032 · Cited by 62 · see citing works
Liu, F., Duan, C., & Kulik, H. J. (2020) · The Journal of Physical Chemistry Letters · doi:10.1021/acs.jpclett.0c02288 · Cited by 58 · see citing works
Combining phonon accuracy with high transferability in Gaussian approximation potential models
George, J., Hautier, G., Bartók, A. P., Csányi, G., & Deringer, V. L. (2020) · The Journal of Chemical Physics · doi:10.1063/5.0013826 · Cited by 44 · see citing works
Protein sequence‐to‐structure learning: Is this the end(‐to‐end revolution)?
Laine, E., Eismann, S., Elofsson, A., & Grudinin, S. (2021) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.26235 · Cited by 44 · see citing works
“These architectures can be seen as equivariant extensions of neural-network-based radial and angular symmetry functions for molecular structure [124, 125, 126].”
BackgroundEnumeration of <i>de novo</i> inorganic complexes for chemical discovery and machine learning
Gugler, S., Janet, J. P., & Kulik, H. J. (2020) · Molecular Systems Design & Engineering · doi:10.1039/c9me00069k · Cited by 43 · see citing works
Predicting Structural Properties of Pure Silica Zeolites Using Deep Neural Network Potentials
Sours, T. G., & Kulkarni, A. R. (2023) · The Journal of Physical Chemistry C · doi:10.1021/acs.jpcc.2c08429 · Cited by 36 · see citing works
Chang, A. M., Freeze, J. G., & Batista, V. S. (2019) · Chemical Science · doi:10.1039/c9sc02339a · Cited by 21 · see citing works
- Authors
- Smith, J. S., Isayev, O., & Roitberg, A. E.
- Affiliations
- University of Florida (US)
- University of North Carolina at Chapel Hill (US)
- Corresponding author
- Isayev, O.
- Roitberg, A. E.
- Venue
- Chemical Science
- Publisher
- Royal Society of Chemistry (RSC)
- Published
- 2017
- Volume
- 8
- Issue
- 4
- Pages
- 3192-3203
- ISSN
- 2041-6520 (ISSN-L)
- 2041-6539
- PMC
- PMC5414547
- OpenAlex
- W2541404351
- Cited by
- 1,921
- References
- 47
- Citations by year
- 2026: 182 · 2025: 286 · 2024: 242 · 2023: 237 · 2022: 222 · 2021: 235 · 2020: 241 · 2019: 167 · 2018: 86 · 2017: 20 · 2012: 1
- FWCI
- 59.53
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- cc-by-nc
- Locations
- Chemical Science (publishedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion, other-oa)
- arXiv (Cornell University)
- Funders
- Office of Naval Research Global (N00014-13-1-0028)
- National Science Foundation (ACI-1053575)
- National Institute of General Medical Sciences (GM110077)
- Office of Naval Research (N00014-16-1-2311)
- University of Florida
- U.S. Department of Energy (400482)
- Topics
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Advanced Chemical Physics Studies
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Keywords
- neural network potentials, transferable interatomic potentials, organic compounds, molecular energy prediction, molecular conformation, force field accuracy
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
