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Introduction: Machine Learning at the Atomic Scale.
Chem Rev. 2021 Aug 25;121(16):9719-9721. doi: 10.1021/acs.chemrev.1c00598.
Chem Rev. 2021.
PMID: 34428897
No abstract available.
Machine learning meets chemical physics.
Ceriotti M, Clementi C, Anatole von Lilienfeld O.
Ceriotti M, et al. Among authors: anatole von lilienfeld o.
J Chem Phys. 2021 Apr 28;154(16):160401. doi: 10.1063/5.0051418.
J Chem Phys. 2021.
PMID: 33940847
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FCHL revisited: Faster and more accurate quantum machine learning.
Christensen AS, Bratholm LA, Faber FA, Anatole von Lilienfeld O.
Christensen AS, et al. Among authors: anatole von lilienfeld o.
J Chem Phys. 2020 Jan 31;152(4):044107. doi: 10.1063/1.5126701.
J Chem Phys. 2020.
PMID: 32007071
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Properties and reactivity of nucleic acids relevant to epigenomics, transcriptomics, and therapeutics.
Gillingham D, Geigle S, Anatole von Lilienfeld O.
Gillingham D, et al. Among authors: anatole von lilienfeld o.
Chem Soc Rev. 2016 May 3;45(9):2637-55. doi: 10.1039/c5cs00271k.
Chem Soc Rev. 2016.
PMID: 26992131
Review.
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Blind test of density-functional-based methods on intermolecular interaction energies.
Taylor DE, Ángyán JG, Galli G, Zhang C, Gygi F, Hirao K, Song JW, Rahul K, Anatole von Lilienfeld O, Podeszwa R, Bulik IW, Henderson TM, Scuseria GE, Toulouse J, Peverati R, Truhlar DG, Szalewicz K.
Taylor DE, et al. Among authors: anatole von lilienfeld o.
J Chem Phys. 2016 Sep 28;145(12):124105. doi: 10.1063/1.4961095.
J Chem Phys. 2016.
PMID: 27782652
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Accurate ab initio energy gradients in chemical compound space.
Anatole von Lilienfeld O.
Anatole von Lilienfeld O.
J Chem Phys. 2009 Oct 28;131(16):164102. doi: 10.1063/1.3249969.
J Chem Phys. 2009.
PMID: 19894922
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GPU-accelerated approximate kernel method for quantum machine learning.
Browning NJ, Faber FA, Anatole von Lilienfeld O.
Browning NJ, et al. Among authors: anatole von lilienfeld o.
J Chem Phys. 2022 Dec 7;157(21):214801. doi: 10.1063/5.0108967.
J Chem Phys. 2022.
PMID: 36511559
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