Tkatchenko A - Search Results

1

Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems.

Keith JA, Vassilev-Galindo V, Cheng B, Chmiela S, Gastegger M, Müller KR, Tkatchenko A. Keith JA, et al. Among authors: tkatchenko a. Chem Rev. 2021 Aug 25;121(16):9816-9872. doi: 10.1021/acs.chemrev.1c00107. Epub 2021 Jul 7. Chem Rev. 2021. PMID: 34232033 Free PMC article.

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

Quantum-chemical insights from deep tensor neural networks.

Schütt KT, Arbabzadah F, Chmiela S, Müller KR, Tkatchenko A. Schütt KT, et al. Among authors: tkatchenko a. Nat Commun. 2017 Jan 9;8:13890. doi: 10.1038/ncomms13890. Nat Commun. 2017. PMID: 28067221 Free PMC article.

6

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.

7

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

8

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.

9

SchNetPack: A Deep Learning Toolbox For Atomistic Systems.

Schütt KT, Kessel P, Gastegger M, Nicoli KA, Tkatchenko A, Müller KR. Schütt KT, et al. Among authors: tkatchenko a. J Chem Theory Comput. 2019 Jan 8;15(1):448-455. doi: 10.1021/acs.jctc.8b00908. Epub 2018 Dec 10. J Chem Theory Comput. 2019. PMID: 30481453

10

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

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