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Machine learning (ML) approaches enable large-scale atomistic simulations with near-quantum-mechanical accuracy.
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A. P. Thompson, L. P. Swiler, C. R. Trott, S. M. Foiles, and G. J. Tucker, “Spectral neighbor analysis method for automated generation of quantum-accurate interatomic potentials,” J. Comput. Phys. 285
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A. C. Forse, C. Merlet, P. K. Allan, E. K. Humphreys, J. M. Griffin, M. Aslan, M. Zeiger, V. Presser, Y. Gogotsi, and C. P. Grey, “New insights into the structure of nanoporous carbons from NMR, Raman, and pair distribution function analysis,” Chem. Mater. 27
2015
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C. de Tomas, I. Suarez-Martinez, and N. A. Marks, “Graphitization of amorphous carbons: A comparative study of interatomic potentials,” Carbon 109
2016
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N. Artrith and A. Urban, “An implementation of artificial neural-network potentials for atomistic materials simulations: Performance for TiO 2
2016
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A. Shapeev, “Moment tensor potentials: A class of systematically improvable interatomic potentials,” Multiscale Model. Simul. 14
2016
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J. Behler, “First principles neural network potentials for reactive simulations of large molecular and condensed systems,” Angew. Chem. Int. Ed. 56
2017
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E. V. Podryabinkin and A. V. Shapeev, “Active learning of linearly parametrized interatomic potentials,” Comput. Mater. Sci. 140
2017
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J. S. Smith, O. Isayev, and A. E. Roitberg, “Ani-1: An extensible neural network potential with DFT accuracy at force field computational cost,” Chem. Sci. 8
2017
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V. L. Deringer and G. Csányi, “Machine learning based interatomic potential for amorphous carbon,” Phys. Rev. B 95
2017
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M. Gastegger, J. Behler, and P. Marquetand, “Machine learning molecular dynamics for the simulation of infrared spectra,” Chem. Sci. 8
2017
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R. A. Mata and M. A. Suhm, “Benchmarking quantum chemical methods: Are we heading in the right direction?” Angew. Chem. Int. Ed. 56
2017
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M. A. Caro, V. L. Deringer, J. Koskinen, T. Laurila, and G. Csányi, “Growth mechanism and origin of high s p 3 s{p}^{3} content in tetrahedral amorphous carbon,” Phys. Rev. Lett. 120
2018
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N. Artrith, A. Urban, and G. Ceder, “Constructing first-principles phase diagrams of amorphous Li x
2018
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K. T. Schütt, H. E. Sauceda, P.-J. Kindermans, A. Tkatchenko, and K.-R. Müller, “SchNet – a deep learning architecture for molecules and materials,” J. Chem. Phys. 148
2018
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A. Glielmo, C. Zeni, and A. De Vita, “Efficient nonparametric n n -body force fields from machine learning,” Phys. Rev. B 97
2018
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V. L. Deringer, C. J. Pickard, and G. Csányi, “Data-driven learning of total and local energies in elemental boron,” Phys. Rev. Lett. 120
2018
Cited alongside, same era.
L. Zhang, J. Han, H. Wang, R. Car, and W. E, “Deep potential molecular dynamics: A scalable model with the accuracy of quantum mechanics,” Phys. Rev. Lett. 120
2018
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A. P. Bartók, J. Kermode, N. Bernstein, and G. Csányi, “Machine learning a general-purpose interatomic potential for silicon,” Phys. Rev. X 8
2018
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V. L. Deringer, N. Bernstein, A. P. Bartók, M. J. Cliffe, R. N. Kerber, L. E. Marbella, C. P. Grey, S. R. Elliott, and G. Csányi, “Realistic atomistic structure of amorphous silicon from machine-learning-driven molecular dynamics,” J. Phys. Chem. Lett. 9
2018
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D. P. Kovács, C. van der Oord, J. Kucera, A. E. A. Allen, D. J. Cole, C. Ortner, and G. Csányi, “Linear atomic cluster expansion force fields for organic molecules: Beyond RMSE,” J. Chem. Theory Comput. 17
2021
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G. Vishwakarma, A. Sonpal, and J. Hachmann, “Metrics for benchmarking and uncertainty quantification: Quality, applicability, and best practices for machine learning in chemistry,” Trends Chem. 3
2021
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N. Artrith, K. T. Butler, F.-X. Coudert, S. Han, O. Isayev, A. Jain, and A. Walsh, “Best practices in machine learning for chemistry,” Nat. Chem. 13
2021
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A. M. Miksch, T. Morawietz, J. Kästner, A. Urban, and N. Artrith, “Strategies for the construction of machine-learning potentials for accurate and efficient atomic-scale simulations,” Mach. Learn.: Sci. Technol. 2
2021
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2019
Cited alongside, same era.
K. Konstantinou, F. C. Mocanu, T.-H. Lee, and S. R. Elliott, “Revealing the intrinsic nature of the mid-gap defects in amorphous Ge 2
2019
Cited alongside, same era.
K. Gubaev, E. V. Podryabinkin, G. L. W. Hart, and A. V. Shapeev, “Accelerating High-Throughput Searches for New Alloys with Active Learning of Interatomic Potentials,” Comput. Mater. Sci. 156
2019
Cited alongside, same era.
C. de Tomas, A. Aghajamali, J. L. Jones, D. J. Lim, M. J. López, I. Suarez-Martinez, and N. A. Marks, “Transferability in interatomic potentials for carbon,” Carbon 155
2019
Cited alongside, same era.
L. Zhang, D.-Y. Lin, H. Wang, R. Car, and W. E, “Active learning of uniformly accurate interatomic potentials for materials simulation,” Phys. Rev. Mater. 3
2019
Cited alongside, same era.
R. Drautz, “Atomic cluster expansion for accurate and transferable interatomic potentials,” Phys. Rev. B 99
2019
Cited alongside, same era.
N. Bernstein, G. Csányi, and V. L. Deringer, “De novo exploration and self-guided learning of potential-energy surfaces,” npj Comput. Mater. 5
2019
Cited alongside, same era.
D. Zagorac, H. Müller, S. Ruehl, J. Zagorac, and S. Rehme, “Recent developments in the Inorganic Crystal Structure Database: theoretical crystal structure data and related features,” J. Appl. Crystallogr. 52
2019
Cited alongside, same era.
J. Behler and G. Csányi, “Machine learning potentials for extended systems: A perspective,” Eur. Phys. J. B 94
2021
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F. Musil, A. Grisafi, A. P. Bartók, C. Ortner, G. Csányi, and M. Ceriotti, “Physics-inspired structural representations for molecules and materials,” Chem. Rev. 121
2021
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J. Behler, “Four generations of high-dimensional neural network potentials,” Chem. Rev. 121
2021
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M. Pinheiro, F. Ge, N. Ferré, P. O. Dral, and M. Barbatti, “Choosing the right molecular machine learning potential,” Chem. Sci. 12
2021
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J. Westermayr, M. Gastegger, K. T. Schütt, and R. J. Maurer, “Perspective on integrating machine learning into computational chemistry and materials science,” J. Chem. Phys. 154
2021
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J. Westermayr, M. Gastegger, D. Vörös, L. Panzenboeck, F. Joerg, L. González, and P. Marquetand, “Deep learning study of tyrosine reveals that roaming can lead to photodamage,” Nat. Chem. 14
2022
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C. G. Staacke, T. Huss, J. T. Margraf, K. Reuter, and C. Scheurer, “Tackling structural complexity in Li 2
2022
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D. Marchand and W. A. Curtin, “Machine learning for metallurgy IV: A neural network potential for Al-Cu-Mg and Al-Cu-Mg-Zn,” Phys. Rev. Mater. 6
2022
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A. Bender, N. Schneider, M. Segler, W. Patrick Walters, O. Engkvist, and T. Rodrigues, “Evaluation guidelines for machine learning tools in the chemical sciences,” Nat. Rev. Chem. 6
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P. Liu, C. Verdi, F. Karsai, and G. Kresse, “Phase transitions of zirconia: Machine-learned force fields beyond density functional theory,” Phys. Rev. B 105
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D. Bayerl, C. M. Andolina, S. Dwaraknath, and W. A. Saidi, “Convergence acceleration in machine learning potentials for atomistic simulations,” Digital Discovery 1
2022
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J. D. Morrow and V. L. Deringer, “Indirect learning and physically guided validation of interatomic potential models,” J. Chem. Phys. 157
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S. Batzner, A. Musaelian, L. Sun, M. Geiger, J. P. Mailoa, M. Kornbluth, N. Molinari, T. E. Smidt, and B. Kozinsky, “E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials,” Nat. Commun. 13
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C. J. Pickard, “Ephemeral data derived potentials for random structure search,” Phys. Rev. B 106
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L. C. Erhard, J. Rohrer, K. Albe, and V. L. Deringer, “A machine-learned interatomic potential for silica and its relation to empirical models,” npj Comput. Mater. 8
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D. Golze, M. Hirvensalo, P. Hernández-León, A. Aarva, J. Etula, T. Susi, P. Rinke, T. Laurila, and M. A. Caro, “Accurate computational prediction of core-electron binding energies in carbon-based materials: A machine-learning model combining density-functional theory and GW,” Chem. Mater. 34
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A. V. Shapeev, D. Bocharov, and A. Kuzmin, “Validation of moment tensor potentials for fcc and bcc metals using EXAFS spectra,” Comput. Mater. Sci. 210
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