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PANNA: Properties from Artificial Neural Network Architectures
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“Existing efforts typically train models per material, with data often sampled from ab initio molecular dynamics (AIMD). This markedly limits their general applicability and adoption, requiring expensive data collection and training a new potential from scratch for each system.”
From Scaling deep learning for materials discovery (2023) · cites this paper as [49]
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Cites
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
Principal component analysis: A review and recent developments
Jolliffe, I. T., & Cadima, J. (2016) · Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences · doi:10.1098/rsta.2015.0202 · Cited by 10,117
Advanced capabilities for materials modelling with Q uantum ESPRESSO
Giannozzi, P., Andreussi, O., Brumme, T., Bunau, O., Buongiorno Nardelli, M., Calandra, M., Car, R., Cavazzoni, C., Ceresoli, D., Cococcioni, M., et al. (2017) · Journal of Physics: Condensed Matter · doi:10.1088/1361-648x/aa8f79 · Cited by 8,361
Self-citationOn representing chemical environments
Bartók, A. P., Kondor, R., & Csányi, G. (2013) · Physical Review B · doi:10.1103/physrevb.87.184115 · Cited by 2,572
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
ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost
Smith, J. S., Isayev, O., & Roitberg, A. E. (2017) · Chemical Science · doi:10.1039/c6sc05720a · Cited by 1,921
Mounet, N., Gibertini, M., Schwaller, P., Campi, D., Merkys, A., Marrazzo, A., Sohier, T., Castelli, I. E., Cepellotti, A., Pizzi, G., et al. (2018) · Nature Nanotechnology · doi:10.1038/s41565-017-0035-5 · Cited by 1,863
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
Machine learning for the structure–energy–property landscapes of molecular crystals
Musil, F., De, S., Yang, J., Campbell, J. E., Day, G. M., & Ceriotti, M. (2018) · Chemical Science · doi:10.1039/c7sc04665k · Cited by 216
Research works printed but not linked: 57
- Montavon, 2013, New J. Phys., 15, 095003, 10.1088/1367-2630/15/9/095003 · DOI
- Hirn, 2017, Multiscale Model. Simul., 15, 827, 10.1137/16M1075454 · DOI
- Yao, 2018, Chem. Sci., 9, 2261, 10.1039/C7SC04934J · DOI
- Chandrasekaran, 2019, Nano Lett., 5, 22
- Schütt, 2014, Phys. Rev. B, 89, 205118, 10.1103/PhysRevB.89.205118 · DOI
- Isayev, 2017, Nature Commun., 8, 15679, 10.1038/ncomms15679 · DOI
- Rajan, 2018, Chem. Mater., 30, 4031, 10.1021/acs.chemmater.8b00686 · DOI
- Wang, 2018, Comput. Phys. Comm., 228, 178, 10.1016/j.cpc.2018.03.016 · DOI
- Behler, 2007, Phy. Rev. Lett., 98, 146401, 10.1103/PhysRevLett.98.146401 · DOI · Paper page
- Gastegger, 2018, J. Chem. Phys., 148, 241709, 10.1063/1.5019667 · DOI
- Xie, 2018, Phys. Rev. Lett., 120, 145301, 10.1103/PhysRevLett.120.145301 · DOI · Paper page
- Legrain, 2017, Chem. Mater., 29, 6220, 10.1021/acs.chemmater.7b00789 · DOI
- Tsubaki, 2018, J. Phys. Chem. Lett., 9, 5733, 10.1021/acs.jpclett.8b01837 · DOI
- Blank, 1995, J. Chem. Phys., 103, 4129, 10.1063/1.469597 · DOI
- Schütt, 2018, J. Chem. Phys., 148, 241722, 10.1063/1.5019779 · DOI · Paper page
- Bartók, 2010, Phys. Rev. Lett., 104, 136403, 10.1103/PhysRevLett.104.136403 · DOI · Paper page
- Nebgen, 2018, J. Chem. Theory Comput., 14, 4687, 10.1021/acs.jctc.8b00524 · DOI
- Brockherde, 2017, Nature Commun., 8, 872, 10.1038/s41467-017-00839-3 · DOI
- Grisafi, 2019, ACS Cent. Sci., 5, 57, 10.1021/acscentsci.8b00551 · DOI
- Unke, 2019, J. Chem. Theory Comput., 15, 3678, 10.1021/acs.jctc.9b00181 · DOI
- Chmiela, 2019, Comput. Phys. Comm., 240, 38, 10.1016/j.cpc.2019.02.007 · DOI
- Behler, 2011, J. Chem. Phys., 134, 074106, 10.1063/1.3553717 · DOI
- Zhang, 2018, Phys. Rev. Lett., 120, 143001, 10.1103/PhysRevLett.120.143001 · DOI
- Huang, 2019, Phys. Rev. B, 99, 064103, 10.1103/PhysRevB.99.064103 · DOI
- Li, 2015, Phys. Rev. Lett., 114, 096405, 10.1103/PhysRevLett.114.096405 · DOI
- Glielmo, 2017, Phys. Rev. B, 95, 214302, 10.1103/PhysRevB.95.214302 · DOI
- Rowe, 2018, Phys. Rev. B, 97, 054303, 10.1103/PhysRevB.97.054303 · DOI
- Artrith, 2014, Nano Lett., 14, 2670, 10.1021/nl5005674 · DOI
- Kolsbjerg, 2018, Phys. Rev. B, 97, 195424, 10.1103/PhysRevB.97.195424 · DOI
- Zeni, 2018, J. Chem. Phys., 148, 241739, 10.1063/1.5024558 · DOI
- Jain, 2016, APL Mater., 4, 053102, 10.1063/1.4944683 · DOI
- Gossett, 2018, Comput. Mater. Sci., 152, 134, 10.1016/j.commatsci.2018.03.075 · DOI
- Khorshidi, 2016, Comput. Phys. Comm., 207, 310, 10.1016/j.cpc.2016.05.010 · DOI
- Iwasa, 2007, J. Phys. Chem. C, 111, 45, 10.1021/jp063532w · DOI
- Artrith, 2016, Comput. Mater. Sci., 114, 135, 10.1016/j.commatsci.2015.11.047 · DOI
- Singraber, 2019, J. Chem. Theory Comput., 15, 1827, 10.1021/acs.jctc.8b00770 · DOI
- Wang, 2018, Comput. Phys. Comm., 228, 178, 10.1016/j.cpc.2018.03.016 · DOI
- Lee, 2019, Comput. Phys. Comm., 242, 95, 10.1016/j.cpc.2019.04.014 · DOI
- Haghighatlari, 2019, ChemRxiv, 8323271
- Giannozzi, 2017, J. Phys.: Condens. Matter, 29, 465901
- Kresse, 1996, Phys. Rev. B, 54, 11169, 10.1103/PhysRevB.54.11169 · DOI · Paper page
- Onat, 2018, Phys. Rev. B, 97, 094106, 10.1103/PhysRevB.97.094106 · DOI
- Plimpton, 1995, J. Comput. Phys., 117, 1, 10.1006/jcph.1995.1039 · DOI · Paper page
- Tadmor, 2011, JOM, 63, 17, 10.1007/s11837-011-0102-6 · DOI
- Kokalj, 1999, J. Mol. Graph. Model., 17, 176, 10.1016/S1093-3263(99)00028-5 · DOI
- Jolliffe, 2016, Philos. Trans.. Ser. A Math. Phys. Eng. Sci., 374, 20150202
- van der Maaten, 2018, J. Mach. Learn. Res., 9, 2579
- Smith, 2017, Sci. Data, 4, 10.1038/sdata.2017.193 · DOI
- Hohenberg, 1964, Phys. Rev., 136, B864, 10.1103/PhysRev.136.B864 · DOI · Paper page
- Fink, 2007, J. Chem. Inf. Model., 47, 342, 10.1021/ci600423u · DOI
- Stillinger, 1985, Phys. Rev. B, 31, 5262, 10.1103/PhysRevB.31.5262 · DOI
- Bartók, 2018, Phys. Rev. X, 8, 041048
- Bonati, 2018, Phys. Rev. Lett., 121, 10.1103/PhysRevLett.121.265701 · DOI
- Hall, 1967, Phys. Rev., 161, 756, 10.1103/PhysRev.161.756 · DOI
- Wang, 2019, npj Comput. Mater., 5, 125, 10.1038/s41524-019-0261-5 · DOI
- Xie, 2019, Nature Commun., 10, 2667, 10.1038/s41467-019-10663-6 · DOI
- Towns, 2014, Comput. Sci. Eng., 16, 62, 10.1109/MCSE.2014.80 · DOI
Cited by
Sorted by citations
Thompson, A. P., Aktulga, H. M., Berger, R., Bolintineanu, D. S., Brown, W. M., Crozier, P. S., in 't Veld, P. J., Kohlmeyer, A., Moore, S. G., Nguyen, T. D., et al. (2022) · Computer Physics Communications · doi:10.1016/j.cpc.2021.108171 · Cited by 12,598
Scaling deep learning for materials discovery
Merchant, A., Batzner, S., Schoenholz, S. S., Aykol, M., Cheon, G., & Cubuk, E. D. (2023) · Nature · doi:10.1038/s41586-023-06735-9 · Cited by 1,527
Metadata
- Authors
- Lot, R., Pellegrini, F., Shaidu, Y., & Küçükbenli, E.
- Affiliations
- Scuola Internazionale Superiore di Studi Avanzati (IT)
- Corresponding author
- Küçükbenli, Emine
- Venue
- Computer Physics Communications
- Publisher
- Elsevier BV
- Published
- 2020-11
- Volume
- 256
- Pages
- 107402
- ISSN
- 0010-4655 (ISSN-L)
- OpenAlex
- W2955651397
- Cited by
- 64
- References
- 9
- Citations by year
- 2026: 5 · 2025: 11 · 2024: 11 · 2023: 13 · 2022: 8 · 2021: 15 · 2020: 1
- FWCI
- 3.46
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Computer Physics Communications (acceptedVersion)
- arXiv (Cornell University) (submittedVersion)
- OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
- SDL (submittedVersion)
- arXiv (Cornell University)
- Funders
- Basic Energy Sciences
- Horizon 2020
- 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 › Artificial Intelligence › Neural Networks and Applications
- Keywords
- interatomic potentials, neural network potentials, machine learning interatomic potentials, force field generation, LAMMPS, TensorFlow, materials property prediction, activation functions
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