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The dream of machine learning in materials science is for a model to learn the underlying physics of an atomic system, allowing it to move beyond interpolation of the training set to the prediction of properties that were not present in the original training data.
1911
Earlier work this paper cites.
F. Birch, “Finite elastic strain of cubic crystals,” Phys. Rev. 71
1947
Earlier work this paper cites.
P. E. Blöchl, “Projector augmented-wave method,” Phys. Rev. B 50
1994
Earlier work this paper cites.
G. Kresse and J. Furthmüller, “Efficient iterative schemes for ab initio total-energy calculations using a plane-wave basis set,” Phys. Rev. B 54
1996
Earlier work this paper cites.
J. P. Perdew, K. Burke, and M. Ernzerhof, “Generalized gradient approximation made simple,” Phys. Rev. Lett. 77
1996
Earlier work this paper cites.
H. Jónsson, G. Mills, and K. W. Jacobsen, “Nudged elastic band method for finding minimum energy paths of transitions,” in Classical and Quantum Dynamics in Condensed Phase Simulations , edited by B. J. Berne, G. Ciccotti, and D. F. Coker (World Scientific, 1998) p. 385
1998
Earlier work this paper cites.
K. . Muller, S. Mika, G. Ratsch, K. Tsuda, and B. Scholkopf, “An introduction to kernel-based learning algorithms,” IEEE Trans. Neur. Netw. 12
2001
Earlier work this paper cites.
2003
Earlier work this paper cites.
T. D. Hatchard and J. R. Dahn, “Study of the electrochemical performance of sputtered Si 1-x
2004
Earlier work this paper cites.
J. Behler and M. Parrinello, “Generalized neural-network representation of high-dimensional potential-energy surfaces,” Phys. Rev. Lett. 98
2007
Earlier work this paper cites.
X.-G. Lu, M. Selleby, and B. Sundman, “Calculations of thermophysical properties of cubic carbides and nitrides using the debye–grüneisen model,” Acta Mater. 55
2007
Earlier work this paper cites.
D. Sholl and J. Steckel, Density Functional Theory: A Practical Introduction (Wiley, 2009)
2009
Earlier work this paper cites.
A. P. Bartók, M. C. Payne, R. Kondor, and G. Csányi, “Gaussian approximation potentials: The accuracy of quantum mechanics, without the electrons,” Phys. Rev. Lett. 104
2010
Earlier work this paper cites.
J. Behler, “Neural network potential-energy surfaces for atomistic simulations,” Chem. Modell. 7
2010
Earlier work this paper cites.
M. Rupp, A. Tkatchenko, K.-R. Müller, and O. A. von Lilienfeld, “Fast and accurate modeling of molecular atomization energies with machine learning,” Phys. Rev. Lett. 108
2012
Earlier work this paper cites.
J. E. Moussa, “Comment on ”fast and accurate modeling of molecular atomization energies with machine learning”,” Phys. Rev. Lett. 109
2012
Earlier work this paper cites.
S. Mallat, “Group invariant scattering,” Comm. Pure Appl. Math. 65
2012
Earlier work this paper cites.
A. P. Bartók, R. Kondor, and G. Csányi, “On representing chemical environments,” Phys. Rev. B 87
2013
Earlier work this paper cites.
G. Montavon, M. Rupp, V. Gobre, A. Vazquez-Mayagoitia, K. Hansen, A. Tkatchenko, K.-R. Müller, and O. A. von Lilienfeld, “Machine learning of molecular electronic properties in chemical compound space,” New J. Phys. 15
2013
Earlier work this paper cites.
2013
Cited alongside, same era.
T. Stecher, N. Bernstein, and G. Csányi, “Free energy surface reconstruction from umbrella samples using gaussian process regression,” J. Chem. Theory Comput. 10
2014
Cited alongside, same era.
Z. Li, J. R. Kermode, and A. De Vita, “Molecular dynamics with on-the-fly machine learning of quantum-mechanical forces,” Phys. Rev. Lett. 114
2015
Cited alongside, same era.
J. Hirschberg and C. D. Manning, “Advances in natural language processing,” Science 349
2015
Cited alongside, same era.
S. De, A. P. Bartok, G. Csanyi, and M. Ceriotti, “Comparing molecules and solids across structural and alchemical space,” Phys. Chem. Chem. Phys. 18
S.-M. Liang, F. Taubert, A. Kozlov, J. Seidel, F. Mertens, and R. Schmid-Fetzer, “Thermodynamics of li-si and li-si-h phase diagrams applied to hydrogen absorption and li-ion batteries,” Intermetallics 81
2017
Later among the works it cites.
K. T. Butler, D. W. Davies, H. Cartwright, O. Isayev, and A. Walsh, “Machine learning for molecular and materials science,” Nature 559
2018
Later among the works it cites.
S. Chmiela, H. E. Sauceda, K.-R. Müller, and A. Tkatchenko, “Towards exact molecular dynamics simulations with machine-learned force fields,” Nat. Commun. 9
2018
Later among the works it cites.
T. Bereau, R. A. DiStasio, A. Tkatchenko, and O. A. von Lilienfeld, “Non-covalent interactions across organic and biological subsets of chemical space: Physics-based potentials parametrized from machine learning,” J. Chem. Phys. 148
2018
Later among the works it cites.
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2016
Cited alongside, same era.
A. Shapeev, “Moment tensor potentials: A class of systematically improvable interatomic potentials,” MMS 14
2016
Cited alongside, same era.
I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning (MIT Press, 2016)
2016
Cited alongside, same era.
L. Mones, N. Bernstein, and G. Csányi, “Exploration, sampling, and reconstruction of free energy surfaces with gaussian process regression,” J. Chem. Theory Comput. 12
2016
Cited alongside, same era.
2016
Cited alongside, same era.
T. P. Senftle, S. Hong, M. M. Islam, S. B. Kylasa, Y. Zheng, Y. K. Shin, C. Junkermeier, R. Engel-Herbert, M. J. Janik, H. M. Aktulga, T. Verstraelen, A. Grama, and A. C. T. van Duin, “The reaxff reactive force-field: development, applications and future directions,” npj Comput. Mater. 2
2016
Cited alongside, same era.
S. Chmiela, A. Tkatchenko, H. E. Sauceda, I. Poltavsky, K. T. Schütt, and K.-R. Müller, “Machine learning of accurate energy-conserving molecular force fields,” Sci. Adv. 3
2017
Cited alongside, same era.
F. Brockherde, L. Vogt, L. Li, M. E. Tuckerman, K. Burke, and K.-R. Müller, “By-passing the Kohn-Sham equations with machine learning,” Nat. Commun. 8
2017
Cited alongside, same era.
T. S. Hy, S. Trivedi, H. Pan, B. M. Anderson, and R. Kondor, “Predicting molecular properties with covariant compositional networks,” J. Chem. Phys. 148
2018
Later among the works it cites.
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
Later among the works it cites.
2018
Later among the works it cites.
N. Artrith, A. Urban, and G. Ceder, “Constructing first-principles phase diagrams of amorphous Li x Si \text{Li}_{x}\text{Si} using machine-learning-assisted sampling with an evolutionary algorithm,” J. Chem. Phys. 148
2018
Later among the works it cites.
B. Onat, E. D. Cubuk, B. D. Malone, and E. Kaxiras, “Implanted neural network potentials: Application to Li-Si alloys,” Phy. Rev. B 97
2018
Later among the works it cites.
A. Voulodimos, N. Doulamis, A. Doulamis, and E. Protopapadakis, “Deep learning for computer vision: A brief review,” Comput. Intell. Neurosci. 2018
2018
Later among the works it cites.
A. Gatt and E. Krahmer, “Survey of the state of the art in natural language generation: Core tasks, applications and evaluation,” J. Artif. Intell. Res. 61
2018
Later among the works it cites.
2018
Later among the works it cites.
G. Imbalzano, A. Anelli, D. Giofré, S. Klees, J. Behler, and M. Ceriotti, “Automatic selection of atomic fingerprints and reference configurations for machine-learning potentials,” J. Chem. Phys. 148
2018
Later among the works it cites.
M. Eickenberg, G. Exarchakis, M. Hirn, S. Mallat, and L. Thiry, “Solid harmonic wavelet scattering for predictions of molecule properties,” J. Chem. Phys. 148
2018
Later among the works it cites.
R. Drautz, “Atomic cluster expansion for accurate and transferable interatomic potentials,” Phys. Rev. B 99
2019
Later among the works it cites.
M. W. Swift and Y. Qi, “First-principles prediction of potentials and space-charge layers in all-solid-state batteries,” Phys. Rev. Lett. 122
2019
Later among the works it cites.
P. Mehta, M. Bukov, C.-H. Wang, A. G. Day, C. Richardson, C. K. Fisher, and D. J. Schwab, “A high-bias, low-variance introduction to Machine Learning for physicists,” Phys. Rep. 810
2019
Later among the works it cites.
P.-W. Guan, G. Houchins, and V. Viswanathan, “Uncertainty quantification of DFT-predicted finite temperature thermodynamic properties within the Debye model,” J. Chem. Phys. 151
2019
Later among the works it cites.