Tkatchenko A - Search Results

1

Machine Learning for Molecular Simulation.

Noé F, Tkatchenko A, Müller KR, Clementi C. Noé F, et al. Among authors: tkatchenko a. Annu Rev Phys Chem. 2020 Apr 20;71:361-390. doi: 10.1146/annurev-physchem-042018-052331. Epub 2020 Feb 24. Annu Rev Phys Chem. 2020. PMID: 32092281

2

Fast and accurate modeling of molecular atomization energies with machine learning.

Rupp M, Tkatchenko A, Müller KR, von Lilienfeld OA. Rupp M, et al. Among authors: tkatchenko a. Phys Rev Lett. 2012 Feb 3;108(5):058301. doi: 10.1103/PhysRevLett.108.058301. Epub 2012 Jan 31. Phys Rev Lett. 2012. PMID: 22400967 Free article.

3

Machine Learning Predictions of Molecular Properties: Accurate Many-Body Potentials and Nonlocality in Chemical Space.

Hansen K, Biegler F, Ramakrishnan R, Pronobis W, von Lilienfeld OA, Müller KR, Tkatchenko A. Hansen K, et al. Among authors: tkatchenko a. J Phys Chem Lett. 2015 Jun 18;6(12):2326-31. doi: 10.1021/acs.jpclett.5b00831. J Phys Chem Lett. 2015. PMID: 26113956 Free PMC article.

4

Assessment and Validation of Machine Learning Methods for Predicting Molecular Atomization Energies.

Hansen K, Montavon G, Biegler F, Fazli S, Rupp M, Scheffler M, von Lilienfeld OA, Tkatchenko A, Müller KR. Hansen K, et al. Among authors: tkatchenko a. J Chem Theory Comput. 2013 Aug 13;9(8):3404-19. doi: 10.1021/ct400195d. Epub 2013 Jul 30. J Chem Theory Comput. 2013. PMID: 26584096

5

Machine learning of accurate energy-conserving molecular force fields.

Chmiela S, Tkatchenko A, Sauceda HE, Poltavsky I, Schütt KT, Müller KR. Chmiela S, et al. Among authors: tkatchenko a. Sci Adv. 2017 May 5;3(5):e1603015. doi: 10.1126/sciadv.1603015. eCollection 2017 May. Sci Adv. 2017. PMID: 28508076 Free PMC article.

6

Many-Body Descriptors for Predicting Molecular Properties with Machine Learning: Analysis of Pairwise and Three-Body Interactions in Molecules.

Pronobis W, Tkatchenko A, Müller KR. Pronobis W, et al. Among authors: tkatchenko a. J Chem Theory Comput. 2018 Jun 12;14(6):2991-3003. doi: 10.1021/acs.jctc.8b00110. Epub 2018 May 31. J Chem Theory Comput. 2018. PMID: 29750522

7

Towards exact molecular dynamics simulations with machine-learned force fields.

Chmiela S, Sauceda HE, Müller KR, Tkatchenko A. Chmiela S, et al. Among authors: tkatchenko a. Nat Commun. 2018 Sep 24;9(1):3887. doi: 10.1038/s41467-018-06169-2. Nat Commun. 2018. PMID: 30250077 Free PMC article.

8

Molecular force fields with gradient-domain machine learning: Construction and application to dynamics of small molecules with coupled cluster forces.

Sauceda HE, Chmiela S, Poltavsky I, Müller KR, Tkatchenko A. Sauceda HE, et al. Among authors: tkatchenko a. J Chem Phys. 2019 Mar 21;150(11):114102. doi: 10.1063/1.5078687. J Chem Phys. 2019. PMID: 30901990

9

Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions.

Schütt KT, Gastegger M, Tkatchenko A, Müller KR, Maurer RJ. Schütt KT, et al. Among authors: tkatchenko a. Nat Commun. 2019 Nov 15;10(1):5024. doi: 10.1038/s41467-019-12875-2. Nat Commun. 2019. PMID: 31729373 Free PMC article.

10

Molecular force fields with gradient-domain machine learning (GDML): Comparison and synergies with classical force fields.

Sauceda HE, Gastegger M, Chmiela S, Müller KR, Tkatchenko A. Sauceda HE, et al. Among authors: tkatchenko a. J Chem Phys. 2020 Sep 28;153(12):124109. doi: 10.1063/5.0023005. J Chem Phys. 2020. PMID: 33003761

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