Crossref
journal-article
American Association for the Advancement of Science (AAAS)
Science Advances (221)
Authors
6
- Stefan Chmiela (first)
- Alexandre Tkatchenko (additional)
- Huziel E. Sauceda (additional)
- Igor Poltavsky (additional)
- Kristof T. Schütt (additional)
- Klaus-Robert Müller (additional)
References
36
Referenced
898
10.1103/PhysRevLett.98.146401
10.1063/1.2746232
10.1103/PhysRevLett.104.136403
-
J. Behler, Atom-centered symmetry functions for constructing high-dimensional neural network potentials. J. Chem. Phys. 134, 074106 (2011).
(
10.1063/1.3553717
) / J. Chem. Phys. / Atom-centered symmetry functions for constructing high-dimensional neural network potentials by Behler J. (2011) -
J. Behler, Neural network potential-energy surfaces in chemistry: A tool for large-scale simulations. Phys. Chem. Chem. Phys. 13, 17930–17955 (2011).
(
10.1039/c1cp21668f
) / Phys. Chem. Chem. Phys. / Neural network potential-energy surfaces in chemistry: A tool for large-scale simulations by Behler J. (2011) -
K. V. J. Jose, N. Artrith, J. Behler, Construction of high-dimensional neural network potentials using environment-dependent atom pairs. J. Chem. Phys. 136, 194111 (2011).
(
10.1063/1.4712397
) / J. Chem. Phys. / Construction of high-dimensional neural network potentials using environment-dependent atom pairs by Jose K. V. J. (2011) 10.1103/PhysRevB.87.184115
-
A. P. Bartók, G. Csányi, Gaussian approximation potentials: A brief tutorial introduction. Int. J. Quantum Chem. 115, 1051–1057 (2015).
(
10.1002/qua.24927
) / Int. J. Quantum Chem. / Gaussian approximation potentials: A brief tutorial introduction by Bartók A. P. (2015) -
S. De, A. P. Bartók, G. Csányi, M. Ceriotti, Comparing molecules and solids across structural and alchemical space. Phys. Chem. Chem. Phys. 18, 13754–13769 (2016).
(
10.1039/C6CP00415F
) / Phys. Chem. Chem. Phys. / Comparing molecules and solids across structural and alchemical space by De S. (2016) 10.1103/PhysRevLett.108.058301
-
G. Montavon, M. Rupp, V. Gobre, A. Vazquez-Mayagoitia, K. Hansen, A. Tkatchenko, K.-R. Müller, O. A. von Lilienfeld, Machine learning of molecular electronic properties in chemical compound space. New J. Phys. 15, 095003 (2013).
(
10.1088/1367-2630/15/9/095003
) / New J. Phys. / Machine learning of molecular electronic properties in chemical compound space by Montavon G. (2013) -
K. Hansen, G. Montavon, F. Biegler, S. Fazli, M. Rupp, M. Scheffler, O. A. von Lilienfeld, A. Tkatchenko, K.-R. Müller, Assessment and validation of machine learning methods for predicting molecular atomization energies. J. Chem. Theory Comput. 9, 3404–3419 (2013).
(
10.1021/ct400195d
) / J. Chem. Theory Comput. / Assessment and validation of machine learning methods for predicting molecular atomization energies by Hansen K. (2013) -
K. Hansen, F. Biegler, R. Ramakrishnan, W. Pronobis, O. A. von Lilienfeld, K.-R. Müller, A. Tkatchenko, Machine learning predictions of molecular properties: Accurate many-body potentials and nonlocality in chemical space. J. Phys. Chem. Lett. 6, 2326–2331 (2015).
(
10.1021/acs.jpclett.5b00831
) / J. Phys. Chem. Lett. / Machine learning predictions of molecular properties: Accurate many-body potentials and nonlocality in chemical space by Hansen K. (2015) -
M. Rupp, R. Ramakrishnan, O. A. von Lilienfeld, Machine learning for quantum mechanical properties of atoms in molecules. J. Phys. Chem. Lett. 6, 3309–3313 (2015).
(
10.1021/acs.jpclett.5b01456
) / J. Phys. Chem. Lett. / Machine learning for quantum mechanical properties of atoms in molecules by Rupp M. (2015) -
V. Botu, R. Ramprasad, Learning scheme to predict atomic forces and accelerate materials simulations. Phys. Rev. B 92, 094306 (2015).
(
10.1103/PhysRevB.92.094306
) / Phys. Rev. B / Learning scheme to predict atomic forces and accelerate materials simulations by Botu V. (2015) - M. Hirn, N. Poilvert, S. Mallat, Quantum energy regression using scattering transforms. CoRR arXiv:1502.02077 (2015). / CoRR / Quantum energy regression using scattering transforms by Hirn M. (2015)
10.1063/1.4966192
-
Z. Li, J. R. Kermode, A. De Vita, Molecular dynamics with on-the-fly machine learning of quantum-mechanical forces. Phys. Rev. Lett. 114, 096405 (2015).
(
10.1103/PhysRevLett.114.096405
) / Phys. Rev. Lett. / Molecular dynamics with on-the-fly machine learning of quantum-mechanical forces by Li Z. (2015) -
C. A. Micchelli, M. A. Pontil, On learning vector-valued functions. Neural Comput. 17, 177–204 (2005).
(
10.1162/0899766052530802
) / Neural Comput. / On learning vector-valued functions by Micchelli C. A. (2005) - A. Caponnetto, C. A. Micchelli, M. Pontil, Y. Ying, Universal multi-task kernels. J. Mach. Learn. Res. 9, 1615–1646 (2008). / J. Mach. Learn. Res. / Universal multi-task kernels by Caponnetto A. (2008)
- V. Sindhwani H. Q. Minh A. C. Lozano Scalable matrix-valued kernel learning for high-dimensional nonlinear multivariate regression and granger causality in Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence (UAI’13) 12 to 14 July 2013.
-
B. Matérn Spatial Variation Lecture Notes in Statistics (Springer-Verlag 1986).
(
10.1007/978-1-4615-7892-5
) - I. S. Gradshteyn I. M. Ryzhik Table of Integrals Series and Products A. Jeffrey D. Zwillinger Eds. (Academic Press ed. 7 2007).
-
T. Gneiting, W. Kleiber, M. Schlather, Matérn cross-covariance functions for multivariate random fields. J. Am. Stat. Assoc. 105, 1167–1177 (2010).
(
10.1198/jasa.2010.tm09420
) / J. Am. Stat. Assoc. / Matérn cross-covariance functions for multivariate random fields by Gneiting T. (2010) - H. Helmholtz, Über Integrale der hydrodynamischen Gleichungen, welche den Wirbelbewegungen entsprechen. Angew. Math. 1858, 25–55 (2009). / Angew. Math. / Über Integrale der hydrodynamischen Gleichungen, welche den Wirbelbewegungen entsprechen by Helmholtz H. (2009)
- W. H. Press S. A. Teukolsky W. T. Vetterling B. P. Flannery Numerical Recipes: The Art of Scientific Computing (Cambridge Univ. Press ed. 3 2007).
10.1103/PhysRevLett.77.3865
10.1103/PhysRevLett.102.073005
-
M. Ceriotti, J. More, D. E. Manolopoulos, i-PI: A Python interface for ab initio path integral molecular dynamics simulations. Comput. Phys. Commun. 185, 1019–1026 (2014).
(
10.1016/j.cpc.2013.10.027
) / Comput. Phys. Commun. / i-PI: A Python interface for ab initio path integral molecular dynamics simulations by Ceriotti M. (2014) -
I. Poltavsky, A. Tkatchenko, Modeling quantum nuclei with perturbed path integral molecular dynamics. Chem. Sci. 7, 1368–1372 (2016).
(
10.1039/C5SC03443D
) / Chem. Sci. / Modeling quantum nuclei with perturbed path integral molecular dynamics by Poltavsky I. (2016) - A. J. Smola B. Schölkopf Learning with Kernels: Support Vector Machines Regularization Optimization and Beyond (MIT Press 2001).
10.1103/PhysRevLett.108.253002
-
J. C. Snyder, M. Rupp, K.-R. Müller, K. Burke, Nonlinear gradient denoising: Finding accurate extrema from inaccurate functional derivatives. Int. J. Quantum Chem. 115, 1102–1114 (2015).
(
10.1002/qua.24937
) / Int. J. Quantum Chem. / Nonlinear gradient denoising: Finding accurate extrema from inaccurate functional derivatives by Snyder J. C. (2015) 10.1162/089976698300017467
-
B. Schölkopf, S. Mika, C. J. C. Burges, P. Knirsch, K.-R. Müller, G. Ratsch, A. J. Smola, Input space versus feature space in kernel-based methods. IEEE Trans. Neural Netw. Learn. Syst. 10, 1000–1017 (1999).
(
10.1109/72.788641
) / IEEE Trans. Neural Netw. Learn. Syst. / Input space versus feature space in kernel-based methods by Schölkopf B. (1999) -
K.-R. Müller, S. Mika, G. Rätsch, K. Tsuda, B. Schölkopf, An introduction to kernel-based learning algorithms. IEEE Trans. Neural Netw. Learn. Syst. 12, 181–201 (2001).
(
10.1109/72.914517
) / IEEE Trans. Neural Netw. Learn. Syst. / An introduction to kernel-based learning algorithms by Müller K.-R. (2001)
Dates
Type | When |
---|---|
Created | 8 years, 3 months ago (May 5, 2017, 8:40 p.m.) |
Deposited | 1 year, 7 months ago (Jan. 9, 2024, 11:30 a.m.) |
Indexed | 22 minutes ago (Aug. 21, 2025, 6:32 a.m.) |
Issued | 8 years, 3 months ago (May 5, 2017) |
Published | 8 years, 3 months ago (May 5, 2017) |
Published Print | 8 years, 3 months ago (May 5, 2017) |
Funders
2
Deutsche Forschungsgemeinschaft
10.13039/501100001659
Region: Europe
gov (National government)
Labels
3
- German Research Association
- German Research Foundation
- DFG
Awards
2
- ID0EVBAI16416
- MU 987/20-1
Ministry of Education, Science and Technology
10.13039/501100004085
Region: Asia
gov (National government)
Labels
2
- Korean Ministry of Education, Science and Technology
- MEST
Awards
2
- ID0EQGAI16417
- 2012-005741
@article{Chmiela_2017, title={Machine learning of accurate energy-conserving molecular force fields}, volume={3}, ISSN={2375-2548}, url={http://dx.doi.org/10.1126/sciadv.1603015}, DOI={10.1126/sciadv.1603015}, number={5}, journal={Science Advances}, publisher={American Association for the Advancement of Science (AAAS)}, author={Chmiela, Stefan and Tkatchenko, Alexandre and Sauceda, Huziel E. and Poltavsky, Igor and Schütt, Kristof T. and Müller, Klaus-Robert}, year={2017}, month=may }