Publications

  • 2024

    X. Hou, S. Li, J. Frey, X. Hong,* L. Ackermann,*
    Machine learning-guided yield optimization for palladaelectro-catalyzed annulation reaction,
    Chem 2024, 10, 2283-2294. DOI

    Z-J. Zhang, M. M. Simon, S. Yu, S-W. Li, X. Chen, S. Cattani, X. Hong,* L. Ackermann,*
    Nickel-Catalyzed Atroposelective C–H Alkylation Enabled by Bimetallic Catalysis with Air-Stable Heteroatom-Substituted Secondary Phosphine Oxide Preligands,
    J. Am. Chem. Soc. 2024, 146, 9172-9180. DOI

    M. E. González Laffitte,* K. Weinbauer, T.-L. Phan, N. Beier, N. Domschke, C. Flamm, T. Gatter, D. Merkle, P. F. Stadler,*
    Partial Imaginary Transition State (ITS) Graphs: A Formal Framework for Research and Analysis of Atom-to-Atom Maps of Unbalanced Chemical Reactions and Their Completions,
    Symmetry 2024, 16, 1217. DOI

    V. Vinod,* P. Zaspel,
    Assessing non-nested configurations of multifidelity machine learning for quantum-chemical properties,
    Mach. Learn.: Sci. Technol. 2024, 5, 045005. DOI

    M. Ruth, T. Gensch,* P. R. Schreiner,*
    Contrasting Historical and Physical Perspectives in Asymmetric Catalysis: ∆∆G‡ versus enantiomeric excess,
    Angew. Chem. Int. Ed. 2024, 63, Early View. DOI

    M. Gubler, J. A. Finkler, M. R. Schäfer, J. Behler, S. Goedecker,
    Accelerating Fourth-Generation Machine Learning Potentials Using Quasi-Linear Scaling Particle Mesh Charge Equilibration,
    J. Chem. Theory Comput. 2024, 20, 7264-7271. DOI

    M. Friede, C. Hölzer, S. Ehlert, S. Grimme,*
    dxtb—An efficient and fully differentiable framework for extended tight-binding,
    J. Chem. Phys. 2024, 161, 062501. DOI

    A.-M. Illig, N. E. Siedhoff, M. D. Davari,* U. Schwaneberg,*
    Evolutionary Probability and Stacked Regressions Enable Data-Driven Protein Engineering with Minimized Experimental Effort,
    J. Chem. Inf. Model. 2024, 64, 6350-6360. DOI

    O. Pereira, M. Ruth, D. Gerbig, R. C. Wende, P. R. Schreiner,*
    Leveraging Limited Experimental Data with Machine Learning: Differentiating a Methyl from an Ethyl Group in the CBS-Reduction,
    J. Am. Chem. Soc. 2024, 146, 14576-14586. DOI

    M. Goles, A. Daza, G. Cabas-Mora, L. Sarmiento-Varón, J. Sepúlveda-Yañez, H. Anvari-Kazemabad, M. D. Davari, R. Uribe-Paredes, Á. Olivera-Nappa, M. A. Navarrete, D. Medina-Ortiz,*
    Peptide-based drug discovery through artificial intelligence: towards an autonomous design of therapeutic peptides,
    Briefings in Bioinformatics 2024, 25 (4), bbae275. DOI

    M. Sokolov, D. S. Hoffmann, P. M. Dohmen, M. Krämer, S. Höfener, U. Kleinekathöferc, M. Elstner,*   
    Non-adiabatic molecular dynamics simulations provide new insights into the exciton transfer in the Fenna–Matthews–Olson complex,
    Phys. Chem. Chem. Phys. 2024, 26, 19469-19496. DOI

    T. Huang, R. Geitner,* A. Croy, S. Gräfe,*
    Tailoring Phosphine Ligands for Improved C‑H Activation: Insights from Δ-Machine Learning,
    Digital Discovery 2024, 3, 1350-1364. DOI

    V. Vinod, U. Kleinekathöfer, P. Zaspel,*
    Optimized multifidelity machine learning for quantum chemistry,
    Mach. Learn.: Sci. Technol. 2024, 5, 015054. DOI

    L. Schlosser, D. Rana, P. Pflüger, F. Katzenburg, F. Glorius,*
    EnTdecker – A machine learning-based platform for guiding substrate discovery in energy transfer catalysis,
    J. Am. Chem. Soc. 2024, 146, 13266-13275. DOI

    A. Croy,
    From Local Atomic Environments to Molecular Information Entropy,
    ACS omega 2024, 9, 20616-20622. DOI

    F. Strieth-Kalthoff,§ S. Szymkuc,§ K. Molga,§ A. Aspuru-Guzik, F. Glorius,* B. A. Grzybowski,*
    Artificial Intelligence for Retrosynthetic Planning Needs Both Data and Expert Knowledge,
    J. Am. Chem. Soc. 2024, 146, 11005-11017. DOI
    § These authors contributed equally.

    D. Rana, P. M. Pflüger, N. P. Hölter, G. Tan, F. Glorius,
    Standardizing Substrate Selection: A Strategy toward Unbiased Evaluation of Reaction Generality,
    ACS Cent. Sci. 2024, 10, 899-906. DOI

    M. L. Schrader,§ F. R. Schäfer,§ F. Schäfers,§ F. Glorius,
    Bridging the information gap in organic chemical reactions,
    Nat. Chem. 2024, 16, 491-498. DOI
    § These authors contributed equally.

    P. M. Pflüger,§ M. Kühnemund,§ F. Katzenburg,§ H. Kuchen, F. Glorius,
    An evolutionary algorithm for interpretable molecular representations,
    Chem 2024, 10, 1391-1405. DOI
    § These authors contributed equally; selected for cover image.

    F. Zills,* M. R. Schäfer,* N. Segreto, J. Kästner, C. Holm, S. Tovey,
    Collaboration on Machine-Learned Potentials with IPSuite: A Modular Framework for Learning-on-the-Fly,
    J. Phys. Chem. B 2024, 128, 36623676. DOI

    F. Zills, M. R. Schäfer, S. Tovey, J. Kästner, C. Holm,
    Machine Learning-Driven Investigation of the Structure and Dynamics of the BMIM-BF₄ Room Temperature Ionic Liquid,
    Faraday Discuss. 2024, Accepted. DOI

  • 2023

    A. M. Tokita,* J. Behler,
    How to train a neural network potential,
    J. Chem. Phys. 2023, 159, 121501. DOI

    M. Ruth, D. Gerbig, P. R. Schreiner,*
    A Machine Learning Approach for Bridging the Gap between Density Functional and Coupled Cluster Theories,
    J. Chem. Theory Comput. 2023, 19, 4912-4920. DOI

    S. Tovey,* S. Krippendorf,* K. Nikolaou, C. Holm,
    Towards a phenomenological understanding of neural networks: data,
    Mach. Learn.: Sci. Technol. 2023, 4, 035040. DOI

    S. Hemmer, N. E. Siedhoff, S. Werner, G. Ölçücü, U. Schwaneberg, K.-E. Jaeger, M. D. Davari,* U. Krauss,*
    Machine Learning-Assisted Engineering of Light, Oxygen, Voltage Photoreceptor Adduct Lifetime,
    JACS Au 2023, 3, 3311-3323. DOI

    T. Schuett, P. Endres, T. Standau, S. Zechel, R. Q. Albuquerque, C. Brütting, H. Ruckdäschel,* U. S. Schubert,*
    Application of Digital Methods in Polymer Science and Engineering,
    Adv. Funct. Mater. 2023, 34, 2309844. DOI

    M. Ringleb, T. Schuett, S. Zechelab, U. S. Schubert,*
    Best practice for sampling in automated parallel synthesizers,
    Digital Discovery 2023, 2, 1883-1893. DOI

    A. Luc, J. C. A. Oliveira, P. Boos, N. Jacob, L. Ackermann,* J. Wencel-Delord,*
    Double cobalt-catalyzed atroposelective C–H activation: One-step synthesis of atropisomeric indoles bearing vicinal C–C and C–N diaxes,
    Chem Catalysis 2023, 3, 100765. DOI

    Y. Holtkamp, M. Kowalewski, J. Jasche, U. Kleinekathöfer,
    Machine-Learned Correction to Ensemble-Averaged Wave Packet Dynamics,
    J. Chem. Phys. 2023, 159, 094107. DOI

    V. Vinod, S. Maity, P. Zaspel,* U. Kleinekathöfer,*
    Multi-Fidelity Machine Learning for Excited State Energies of Molecules,
    J. Chem. Theory Comput. 2023, 19, 7658-7670. DOI
    arXiv:2305.11292 (2023). DOI

    F. Jirasek, H. Hasse,
    Combining Machine Learning with Physical Knowledge in Thermodynamic Modeling of Fluid Mixtures,
    Annu. Rev. Chem. Biomol. Eng. 2023, 14, 31-51. DOI

    M. Korn, C. Ehrt, F. Ruggiu, M. Gastreich, M. Rarey,*
    Navigating large chemical spaces in early-phase drug discovery,
    Current Opinion in Structural Biology 2023, 80, 102578. DOI
    This review comes from a themed issue on New Concepts in Drug Discovery (2023); Eds. A. R. Leach & A. E. Ondrus.

    S. Grimme,* M. Müller, A. Hansen,
    A non-self-consistent tight-binding electronic structure potential in a polarized double-ζ basis set for all spd-block elements up to Z = 86,
    J. Chem. Phys. 2023, 158, 124111. DOI

    Z.-J. Zhang, S.-W. Li, J. C. A. Oliveira, Y. Li, X. Chen, S.-Q. Zhang, L.-C. Xu, T. Rogge, X. Hong,* L. Ackermann,*
    Data-driven design of new chiral carboxylic acid for construction of indoles with C-central and C–N axial chirality via cobalt catalysis,  
    Nat Commun 2023, 14, 3149. DOI

    L.-C. Xu, J. Frey, X. Hou, S.-Q. Zhang, Y.-Y. Li, J. C. A. Oliveira, S.-W. Li, L. Ackermann,* X. Hong,*
    Enantioselectivity prediction of pallada-electrocatalysed C–H activation using transition state knowledge in machine learning,   
    Nat. Synth 2023, 2, 321-330. DOI

    S.-Q. Zhang, L.-C. Xu, S.-W. Li, J. C. A. Oliveira, X. Li, L. Ackermann,* X. Hong,*
    Bridging Chemical Knowledge and Machine Learning for Performance Prediction of Organic Synthesis,
    Chem. Eur.  J. 2023, 29,  e2022028. DOI

    J. Löffler, S. M. P. Vanden Broeck, C. S. J. Cazin, S. P. Nolan, V. H. Gessner,*
    Correlation of Experimental and Calculated Reaction Enthalpies with Ligand Donor Strengths,
    Chem. Eur. J. 2023, 29, e202300151. DOI

    S. Tovey, F. Zills, F. Torres-Herrador, C. Lohrmann, M. Brückner, C. Holm,*
    MDSuite: comprehensive post-processing tool for particle simulations,
    J Cheminform 2023, 15, article number 19. DOI

    M. E. González Laffitte, N. Beier, N. Domschke, P. F. Stadler,*
    Comparison of Atom Maps,
    MATCH Commun. Math. Comput. Chem. 2023, 90, 75-102. DOI

    J. F. Goebel, J. Löffler, Z. Zeng, J. Handelmann, A. Hermann, I. Rodstein, T. Gensch, V. H. Gessner,* L. J. Gooßen,*
    Computer-Driven Development of Ylide Functionalized Phosphinesfor Palladium-Catalyzed Hiyama Couplings,
    Angew. Chem. Int. Ed. 2023, 62, e202216160. DOI

    A. McSloy, G. Fan, W. Sun, C. Hölzer, M. Friede, S. Ehlert, N.-E. Schütte, S. Grimme, T. Frauenheim, B. Aradi,
    TBMaLT, a flexible toolkit for combining tight-binding and machine learning,
    J. Chem. Phys. 2023, 158, 034801. DOI

    M. Müller, A. Hansen, S. Grimme,*
    ωB97X-3c: A composite range-separated hybrid DFT method with a molecule-optimized polarized valence double-ζ basis set,
    J. Chem. Phys. 2023, 158, 014103. DOI

  • 2022

    M. Wittmund, F. Cadet, M. D. Davari,*
    Learning Epistasis and Residue Coevolution Patterns: Current Trends and Future Perspectives for Advancing Enzyme Engineering,
    ACS Catal. 2022, 12, 14243-14263. DOI

    M. Bursch,* J.-M. Mewes*, A. Hansen,* S. Grimme,*
    Best-Practice DFT Protocols for Basic Molecular Computational Chemistry,
    Angew. Chem. Int. Ed. 2022, 61 (42), e202205735. DOI

    M. Rarey*, M. C. Nicklaus, W. Warr,
    Special Issue on Reaction Informatics and Chemical Space,
    J. Chem. Inf. Model. 2022, 62, 2009-2010.  DOI

    L. Howes (Ed.),
    Hunting for drugs in chemical space,
    C&EN - Chemical & Engineering News 2022, 100 (23), 20.
    With Front Cover of Issue 23 and Interview with Matthias Rarey.  Link

    F. Strieth-Kalthoff,§ F. Sandfort,§ M. Kühnemund, F. R. Schäfer, H. Kuchen, F. Glorius,
    Machine Learning for Chemical Reactivity: The Importance of Failed Experiments,
    Angew. Chem. Int. Ed. 2022, 61,     e202204647. DOI
    § These authors contributed equally
    Dedicated to the late Professor David A. Evans

    F. Jirasek, R. Bamler, S. Fellenz, M. Bortz, M. Kloft, S. Mandt, H. Hasse,
    Making thermodynamic models of mixtures predictive by machine learning: matrix completion of pair interactions,
    Chem. Sci. 2022, 13, 4854-4862. DOI