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Atomic cluster expansion for accurate and transferable interatomic potentials
Abstract
The atomic cluster expansion is developed as a complete descriptor of the local atomic environment, including multicomponent materials, and its relation to a number of other descriptors and potentials is discussed. The effort for evaluating the atomic cluster expansion is shown to scale linearly with the number of neighbors, irrespective of the order of the expansion. Application to small Cu clusters demonstrates smooth convergence of the atomic cluster expansion to meV accuracy. By introducing nonlinear functions of the atomic cluster expansion an interatomic potential is obtained that is comparable in accuracy to state-of-the-art machine learning potentials. Because of the efficient convergence of the atomic cluster expansion relevant subspaces can be sampled uniformly and exhaustively. This is demonstrated by testing against a large database of density functional theory calculations for copper.
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Cites
Generalized Gradient Approximation Made Simple
Perdew, J. P., Burke, K., & Ernzerhof, M. (1996) · Physical Review Letters · doi:10.1103/physrevlett.77.3865 · Cited by 215,824
Self-Consistent Equations Including Exchange and Correlation Effects
Kohn, W., & Sham, L. J. (1965) · Physical Review · doi:10.1103/physrev.140.a1133 · Cited by 64,135
Hohenberg, P., & Kohn, W. (1964) · Physical Review · doi:10.1103/physrev.136.b864 · Cited by 53,541
An Algorithm for Least-Squares Estimation of Nonlinear Parameters
Marquardt, D. W. (1963) · Journal of the Society for Industrial and Applied Mathematics · doi:10.1137/0111030 · Cited by 30,728
Accurate and simple analytic representation of the electron-gas correlation energy
Perdew, J. P., & Wang, Y. (1992) · Physical Review B · doi:10.1103/physrevb.45.13244 · Cited by 25,089
Ground State of the Electron Gas by a Stochastic Method
Ceperley, D. M., & Alder, B. J. (1980) · Physical Review Letters · doi:10.1103/physrevlett.45.566 · Cited by 14,384
A method for the solution of certain non-linear problems in least squares
Levenberg, K. (1944) · Quarterly of Applied Mathematics · doi:10.1090/qam/10666 · Cited by 12,359
Daw, M. S., & Baskes, M. I. (1984) · Physical Review B · doi:10.1103/physrevb.29.6443 · Cited by 7,263
Computer simulation of local order in condensed phases of silicon
Stillinger, F. H., & Weber, T. A. (1985) · Physical Review B · doi:10.1103/physrevb.31.5262 · Cited by 5,302
Simplified LCAO Method for the Periodic Potential Problem
Slater, J. C., & Koster, G. F. (1954) · Physical Review · doi:10.1103/physrev.94.1498 · Cited by 5,189
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
Bond-orientational order in liquids and glasses
Steinhardt, P. J., Nelson, D. R., & Ronchetti, M. (1983) · Physical Review B · doi:10.1103/physrevb.28.784 · Cited by 3,475
Gaussian Approximation Potentials: The Accuracy of Quantum Mechanics, without the Electrons
Bartók, A. P., Payne, M. C., Kondor, R., & Csányi, G. (2010) · Physical Review Letters · doi:10.1103/physrevlett.104.136403 · Cited by 3,242
A simple empirical N-body potential for transition metals
Finnis, M. W., & Sinclair, J. E. (1984) · Philosophical Magazine A · doi:10.1080/01418618408244210 · Cited by 3,159
Ab initio molecular simulations with numeric atom-centered orbitals
Blum, V., Gehrke, R., Hanke, F., Havu, P., Havu, V., Ren, X., Reuter, K., & Scheffler, M. (2009) · Computer Physics Communications · doi:10.1016/j.cpc.2009.06.022 · Cited by 2,919
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
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
Modified embedded-atom potentials for cubic materials and impurities
Baskes, M. I. (1992) · Physical Review B · doi:10.1103/physrevb.46.2727 · Cited by 2,093
The high-throughput highway to computational materials design
Curtarolo, S., Hart, G. L. W., Nardelli, M. B., Mingo, N., Sanvito, S., & Levy, O. (2013) · Nature Materials · doi:10.1038/nmat3568 · Cited by 2,053
Generalized cluster description of multicomponent systems
Sanchez, J. M., Ducastelle, F., & Gratias, D. (1984) · Physica A: Statistical Mechanics and its Applications · doi:10.1016/0378-4371(84)90096-7 · Cited by 1,812
Empirical interatomic potential for silicon with improved elastic properties
Tersoff, J. (1988) · Physical Review B · doi:10.1103/physrevb.38.9902 · Cited by 1,570
Moment Tensor Potentials: A Class of Systematically Improvable Interatomic Potentials
Shapeev, A. V. (2016) · Multiscale Modeling & Simulation · doi:10.1137/15m1054183 · Cited by 1,510
New empirical model for the structural properties of silicon
Tersoff, J. (1986) · Physical Review Letters · doi:10.1103/physrevlett.56.632 · Cited by 1,387
Machine learning of accurate energy-conserving molecular force fields
Chmiela, S., Tkatchenko, A., Sauceda, H. E., Poltavsky, I., Schütt, K. T., & Müller, K.-R. (2017) · Science Advances · doi:10.1126/sciadv.1603015 · Cited by 1,175
Thompson, A. P., Swiler, L. P., Trott, C. R., Foiles, S. M., & Tucker, G. J. (2015) · Journal of Computational Physics · doi:10.1016/j.jcp.2014.12.018 · Cited by 1,130
Weiße, A., Wellein, G., Alvermann, A., & Fehske, H. (2006) · Reviews of Modern Physics · doi:10.1103/revmodphys.78.275 · Cited by 1,036
Constructing high‐dimensional neural network potentials: A tutorial review
Behler, J. (2015) · International Journal of Quantum Chemistry · doi:10.1002/qua.24890 · Cited by 884
Lattice relaxation at a metal surface
Gupta, R. P. (1981) · Physical Review B · doi:10.1103/physrevb.23.6265 · Cited by 764
Molecular Dynamics with On-the-Fly Machine Learning of Quantum-Mechanical Forces
Li, Z., Kermode, J. R., & De Vita, A. (2015) · Physical Review Letters · doi:10.1103/physrevlett.114.096405 · Cited by 633
Havu, V., Blum, V., Havu, P., & Scheffler, M. (2009) · Journal of Computational Physics · doi:10.1016/j.jcp.2009.08.008 · Cited by 599
Effective-medium theory of chemical binding: Application to chemisorption
Nørskov, J. K., & Lang, N. D. (1980) · Physical Review B · doi:10.1103/physrevb.21.2131 · Cited by 474
Sutton, A. P., Finnis, M. W., Pettifor, D. G., & Ohta, Y. (1988) · Journal of Physics C: Solid State Physics · doi:10.1088/0022-3719/21/1/007 · Cited by 453
Efficient Linear Scaling Algorithm for Tight-Binding Molecular Dynamics
Goedecker, S., & Colombo, L. (1994) · Physical Review Letters · doi:10.1103/physrevlett.73.122 · Cited by 448
Interatomic Forces in Condensed Matter
Finnis, M. (2003) · doi:10.1093/acprof:oso/9780198509776.001.0001 · Cited by 407
Atoms embedded in an electron gas: Immersion energies
Puska, M. J., Nieminen, R. M., & Manninen, M. (1981) · Physical Review B · doi:10.1103/physrevb.24.3037 · Cited by 376
Artrith, N., Urban, A., & Ceder, G. (2017) · Physical Review B · doi:10.1103/physrevb.96.014112 · Cited by 340
High-dimensional neural network potentials for metal surfaces: A prototype study for copper
Artrith, N., & Behler, J. (2012) · Physical Review B · doi:10.1103/physrevb.85.045439 · Cited by 325
Moment Tensor PotentialsPreprint
Shapeev, A. V. (2016) · arXiv (Cornell University) · Cited by 288
Dragoni, D., Daff, T. D., Csányi, G., & Marzari, N. (2018) · Physical Review Materials · doi:10.1103/physrevmaterials.2.013808 · Cited by 284
Manzhos, S., & Carrington, T. (2006) · The Journal of Chemical Physics · doi:10.1063/1.2336223 · Cited by 256
A universal strategy for the creation of machine learning-based atomistic force fields
Huan, T. D., Batra, R., Chapman, J., Krishnan, S., Chen, L., & Ramprasad, R. (2017) · npj Computational Materials · doi:10.1038/s41524-017-0042-y · Cited by 230
Kamath, A., Vargas-Hernández, R. A., Krems, R. V., Carrington, T., & Manzhos, S. (2018) · The Journal of Chemical Physics · doi:10.1063/1.5003074 · Cited by 224
Extending the accuracy of the SNAP interatomic potential form
Wood, M. A., & Thompson, A. P. (2018) · The Journal of Chemical Physics · doi:10.1063/1.5017641 · Cited by 215
New classical models for silicon structural energies
Biswas, R., & Hamann, D. R. (1987) · Physical Review B · doi:10.1103/physrevb.36.6434 · Cited by 210
Bereau, T., DiStasio, R. A., Tkatchenko, A., & von Lilienfeld, O. A. (2018) · The Journal of Chemical Physics · doi:10.1063/1.5009502 · Cited by 175
Nguyen, T. T., Székely, E., Imbalzano, G., Behler, J., Csányi, G., Ceriotti, M., Götz, A. W., & Paesani, F. (2018) · The Journal of Chemical Physics · doi:10.1063/1.5024577 · Cited by 171
Nguyen, T. T. M., Székely, E., Imbalzano, G., Behler, J., Cśanyi, G., Ceriotti, M., Götz, A. W., & Paesani, F. (2018) · Apollo (University of Cambridge) · doi:10.17863/cam.22160 · Cited by 167
Modeling the Phase-Change Memory Material, Ge2Sb2Te5, with a Machine-Learned Interatomic Potential
Mocanu, F. C., Konstantinou, K., Lee, T. H., Bernstein, N., Deringer, V. L., Cśanyi, G., & Elliott, S. R. (2018) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.8b06476 · Cited by 164
Kernel Polynomial Approximations for Densities of States and Spectral Functions
Silver, R. N., Roeder, H., Voter, A. F., & Kress, J. D. (1996) · Journal of Computational Physics · doi:10.1006/jcph.1996.0048 · Cited by 151
Linear-scaling tight binding from a truncated-moment approach
Voter, A. F., Kress, J. D., & Silver, R. N. (1996) · Physical review. B, Condensed matter · doi:10.1103/physrevb.53.12733 · Cited by 149
Drautz, R., & Fähnle, M. (2004) · Physical Review B · doi:10.1103/physrevb.69.104404 · Cited by 111
Self-citationGuest Editorial: Special Topic on Data-Enabled Theoretical Chemistry
Rupp, M., von Lilienfeld, O. A., & Burke, K. (2018) · The Journal of Chemical Physics · doi:10.1063/1.5043213 · Cited by 104
Takahashi, A., Seko, A., & Tanaka, I. (2017) · Physical Review Materials · doi:10.1103/physrevmaterials.1.063801 · Cited by 72
Bond-order potentials: derivation and parameterization for refractory elements
Drautz, R., Hammerschmidt, T., Čák, M., & Pettifor, D. G. (2015) · Modelling and Simulation in Materials Science and Engineering · doi:10.1088/0965-0393/23/7/074004 · Cited by 56
Self-citationParametrization of the magnetic energy at the atomic level
Drautz, R., & Fähnle, M. (2005) · Physical Review B · doi:10.1103/physrevb.72.212405 · Cited by 54
Self-citationElectronic structure based descriptor for characterizing local atomic environments
Jenke, J., Subramanyam, A. P. A., Densow, M., Hammerschmidt, T., Pettifor, D. G., & Drautz, R. (2018) · Physical review. B./Physical review. B · doi:10.1103/physrevb.98.144102 · Cited by 26
Self-citationHammerschmidt, T., Ladines, A. N., Koßmann, J., & Drautz, R. (2016) · Crystals · doi:10.3390/cryst6020018 · Cited by 16
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Batzner, S., Musaelian, A., Sun, L., Geiger, M., Mailoa, J. P., Kornbluth, M., Molinari, N., Smidt, T. E., & Kozinsky, B. (2022) · Nature Communications · doi:10.1038/s41467-022-29939-5 · Cited by 2,285
Four Generations of High-Dimensional Neural Network Potentials
Behler, J. (2021) · Chemical Reviews · doi:10.1021/acs.chemrev.0c00868 · Cited by 873
Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems
Keith, J. A., Vassilev-Galindo, V., Cheng, B., Chmiela, S., Gastegger, M., Müller, K.-R., & Tkatchenko, A. (2021) · Chemical Reviews · doi:10.1021/acs.chemrev.1c00107 · Cited by 849
Hart, G. L. W., Mueller, T., Toher, C., & Curtarolo, S. (2021) · Nature Reviews Materials · doi:10.1038/s41578-021-00340-w · Cited by 703
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Batatia, I., Benner, P., Chiang, Y., Elena, A. M., Kovács, D. P., Riebesell, J., Advincula, X. R., Asta, M., Avaylon, M., Baldwin, W. J., et al. (2024) · arXiv · doi:10.48550/arxiv.2401.00096 · Cited by 604
Metadata
- Authors
- Drautz, R.
- Venue
- Physical Review B
- Publisher
- American Physical Society (APS)
- Published
- 2019-01-08
- Volume
- 99
- Issue
- 1
- ISSN
- 2469-9950 (ISSN-L)
- 2469-9969
- OpenAlex
- W2910857709
- Cited by
- 959
- References
- 57
- FWCI
- 13.81
- Type
- journal article
- Language
- en
- Open access
- closed
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