Publications

Coretti A, Falkner S, Weinreich J, Dellago C, von Lilienfeld-Toal OA. Boltzmann Generators and the New Frontier of Computational Sampling in Many-Body Systems. KIM Review. 2024;2:3. doi: 10.25950/bfa99422

Weinreich J, Lemm D, Von Rudorff GF, von Lilienfeld OA. Ab initio machine learning of phase space averages. Journal of Chemical Physics. 2022 Jul 14;157(2):024303. doi: 10.1063/5.0095674

Domenichini G, von Lilienfeld OA. Alchemical geometry relaxation. Journal of Chemical Physics. 2022 May;156(18):184801. doi: 10.1063/5.0085817

Karandashev K, Von Lilienfeld OA. An orbital-based representation for accurate quantum machine learning. Journal of Chemical Physics. 2022 Mar 21;156(11):114101. Epub 2022 Mar 15. doi: 10.1063/5.0083301

Schwilk M, Mezei PD, Tahchieva DN, von Lilienfeld OA. Non-covalent interactions between molecular dimers (S66) in electric fields. Electronic Structure. 2022 Mar;4(1):014005. doi: 10.1088/2516-1075/ac4eeb

Kilaj A, Wang J, Stranak P, Schwilk M, Rivero U, Xu L et al. Conformer-specific polar cycloaddition of dibromobutadiene with trapped propene ions. Nature Communications. 2021 Oct 18;12(1):6047. doi: 10.1038/s41467-021-26309-5

Huang B, von Lilienfeld OA. Ab Initio Machine Learning in Chemical Compound Space. Chemical Reviews. 2021 Aug 25;121(16):10001-10036. doi: 10.1021/acs.chemrev.0c01303

Ceriotti M, Clementi C, von Lilienfeld OA. Introduction: Machine Learning at the Atomic Scale. Chemical Reviews. 2021 Aug 25;121(16):9719-9721. doi: 10.1021/acs.chemrev.1c00598

Heinen S, von Rudorff GF, von Lilienfeld OA. Toward the design of chemical reactions: Machine learning barriers of competing mechanisms in reactant space. Journal of Chemical Physics. 2021 Aug 14;155(6):064105. doi: 10.1063/5.0059742

Bakowies D, von Lilienfeld OA. Density Functional Geometries and Zero-Point Energies in Ab Initio Thermochemical Treatments of Compounds with First-Row Atoms (H, C, N, O, F). Journal of Chemical Theory and Computation. 2021 Aug 10;17(8):4872-4890. doi: 10.1021/acs.jctc.1c00474

Lemm D, von Rudorff GF, von Lilienfeld OA. Machine learning based energy-free structure predictions of molecules, transition states, and solids. Nature Communications. 2021 Jul 22;12(1):4468. doi: 10.1038/s41467-021-24525-7

Tapavicza E, von Rudorff GF, De Haan DO, Contin M, George C, Riva M et al. Elucidating an Atmospheric Brown Carbon Species-Toward Supplanting Chemical Intuition with Exhaustive Enumeration and Machine Learning. Environmental Science & Technology. 2021 Jun 15;55(12):8447-8457. doi: 10.1021/acs.est.1c00885

von Rudorff GF, von Lilienfeld OA. Simplifying inverse materials design problems for fixed lattices with alchemical chirality. Science Advances. 2021 May 19;7(21). doi: 10.1126/sciadv.abf1173

Ceriotti M, Clementi C, Anatole von Lilienfeld O. Machine learning meets chemical physics. Journal of Chemical Physics. 2021 Apr 28;154(16):160401. Epub 2021 Apr 23. doi: 10.1063/5.0051418

Weinreich J, Browning NJ, Von Lilienfeld OA. Machine learning of free energies in chemical compound space using ensemble representations: Reaching experimental uncertainty for solvation. Journal of Chemical Physics. 2021 Apr 7;154(13):134113. doi: 10.1063/5.0041548

Bragato M, von Rudorff GF, von Lilienfeld OA. Data enhanced Hammett-equation: reaction barriers in chemical space. Chemical Science. 2020 Nov 21;11(43):11859-11868. doi: 10.1039/d0sc04235h

von Lilienfeld OA, Burke K. Retrospective on a decade of machine learning for chemical discovery. Nature Communications. 2020 Sept 29;11(1):4895. doi: 10.1038/s41467-020-18556-9