Adil Kabylda
I am a postdoctoral researcher in the Theoretical Chemical Physics group at the University of Luxembourg, where I recently obtained my Ph.D. under the supervision of Prof. Alexandre Tkatchenko. My research focuses on extending the applicability of machine learning force fields to larger (bio)molecules, with particular emphasis on accurately describing long-range interactions. My ultimate goal is to enable atomistic modeling and understanding of increasingly complex (bio)molecular systems.

I received my B.Sc. and M.Sc. in chemistry from Moscow State University in 2021, specializing in physical and quantum chemistry. During my master's degree, I joined the Quantum Photodynamics Laboratory, where I studied fundamental chemical processes of living organisms using state-of-the-art quantum chemistry methods. These included photo-induced isomerization of retinal, proton and electron transfer in green fluorescent protein. As an undergraduate, I pursued research in organic synthesis, developing efficient routes to naturally occurring alkaloids and designing asymmetric ion-pairing catalysts.

I grew up in Pavlodar, Kazakhstan, and discovered my passion for the natural sciences through high school chemistry olympiads. Besides research, my interests include counter-strike, chess, and russian rap music.

Latest News

06/2026 - Honored to be selected as a delegate at the 56th International Achievement Summit in Washington, DC!
06/2026 - I was featured in the Science category of Forbes Kazakhstan 30u30!
05/2026 - How Atoms Interact Within Molecules is out on arXiv
04/2026 - Received a 2026 Google Academic Research Award to support our MLFF efforts!
04/2026 - I gave an invited talk at ShanghaiTech University (SPST)
03/2026 - I gave an invited talk in the Physics Department at Tsinghua University in Beijing
03/2026 - Our work on QCell is published in AI Sci.
02/2026 - Our work on MBD-ML is out on arXiv

Education

Professional experience

Awards

Teaching and Service

Miscellaneous

Publications

18 publications (incl. 8 as first author); >670 citations on Google Scholar, h-index ≥11.

(Co-)First Author

  1. How Atoms Interact Within Molecules
    A. Kabylda, M. Esders, M. Gori, S. Chmiela, K.-R. Müller, A. Tkatchenko
    arXiv 2026, 2605.28960
  2. QCell: Comprehensive Quantum-Mechanical Dataset Spanning Diverse Biomolecular Fragments
    A. Kabylda, S. Suárez-Dou, N. Davoine, F.N. Brünig, A. Tkatchenko
    AI Sci. 2026, 2, 025003
  3. General-Purpose Machine Learning Force Fields for (Bio)Molecular Simulations
    A. Kabylda
    University of Luxembourg 2026, PhD Thesis
  4. Molecular Simulations with a Pretrained Neural Network and Universal Pairwise Force Fields
    A. Kabylda, J.T. Frank, S. Suárez-Dou, A. Khabibrakhmanov, L. Medrano Sandonas, O.T. Unke, S. Chmiela, K.-R. Müller, A. Tkatchenko
    J. Am. Chem. Soc. 2025, 147, 37, 33723 — Cover article
  5. Mechanical Properties of Nanoporous Graphenes: Transferability of Graph Machine-Learned Force Fields Compared to Local and Reactive Potentials
    A. Kabylda, B. Mortazavi, X. Zhuang, A. Tkatchenko
    Adv. Funct. Mater. 2025, 35, 2417891
  6. Efficient Interatomic Descriptors for Accurate Machine Learning Force Fields of Extended Molecules
    A. Kabylda, V. Vassilev-Galindo, S. Chmiela, I. Poltavsky, A. Tkatchenko
    Nat. Commun. 2023, 14, 3562
  7. Light Driven Ultrafast Bioinspired Molecular Motors: Steering and Accelerating Photoisomerization Dynamics of Retinal
    E. Gruber, A.M. Kabylda, M.B. Nielsen, A.P. Rasmussen, R. Teiwes, P.A. Kusochek, A.V. Bochenkova, L.H. Andersen
    J. Am. Chem. Soc. 2022, 144, 1, 69
  8. A General Mechanism of Green-to-Red Photoconversions of GFP
    D.A. Gorbachev, E.F. Petrusevich, A.M. Kabylda, E.G. Maksimov, K.A. Lukyanov, A.M. Bogdanov, M.S. Baranov, A.V. Bochenkova, A.S. Mishin
    Front. Mol. Biosci. 2020, 7, 176

Contributing Author

  1. MBD-ML: Many-Body Dispersion from Machine Learning for Molecules and Materials
    E. Moerman, A. Kabylda, A. Khabibrakhmanov, A. Tkatchenko
    arXiv 2026, 2602.22086
  2. AI4X Roadmap: Artificial Intelligence for the Advancement of Scientific Pursuit and Its Future Directions
    S.G. Dale, N. Kazeev, A.J.A. Price, …, A. Kabylda, I. Poltavsky, A. Tkatchenko, …, J.-H. Garcia
    arXiv 2025, 2511.20976
  3. Analyzing Atomic Interactions in Molecules as Learned by Neural Networks
    M. Esders, T. Schnake, J. Lederer, A. Kabylda, G. Montavon, A. Tkatchenko, K.-R. Müller
    J. Chem. Theory Comput. 2025, 21, 2, 714
  4. Crash Testing Machine Learning Force Fields for Molecules, Materials, and Interfaces: Molecular Dynamics in the TEA Challenge 2023
    I. Poltavsky, M. Puleva, A. Charkin-Gorbulin, G. Fonseca, I. Batatia, N.J. Browning, S. Chmiela, M. Cui, J.T. Frank, S. Heinen, B. Huang, S. Käser, A. Kabylda, D. Khan, C. Müller, A.J.A. Price, K. Riedmiller, K. Töpfer, T.W. Ko, M. Meuwly, M. Rupp, G. Csányi, O.A. von Lilienfeld, J.T. Margraf, K.-R. Müller, A. Tkatchenko
    Chem. Sci. 2025, 16, 3738
  5. Crash Testing Machine Learning Force Fields for Molecules, Materials, and Interfaces: Model Analysis in the TEA Challenge 2023
    I. Poltavsky, M. Puleva, A. Charkin-Gorbulin, G. Fonseca, I. Batatia, N.J. Browning, S. Chmiela, M. Cui, J.T. Frank, S. Heinen, B. Huang, S. Käser, A. Kabylda, D. Khan, C. Müller, A.J.A. Price, K. Riedmiller, K. Töpfer, T.W. Ko, M. Meuwly, M. Rupp, G. Csányi, O.A. von Lilienfeld, J.T. Margraf, K.-R. Müller, A. Tkatchenko
    Chem. Sci. 2025, 16, 3720
  6. Accurate Global Machine Learning Force Fields for Molecules with Hundreds of Atoms
    S. Chmiela, V. Vassilev-Galindo, O.T. Unke, A. Kabylda, H.E. Sauceda, A. Tkatchenko, K.-R. Müller
    Sci. Adv. 2023, 9, eadf0873
  7. Controlling Light-Induced Proton Transfer from the GFP Chromophore
    J. Langeland, N.W. Persen, E. Gruber, H.V. Kiefer, A.M. Kabylda, A.V. Bochenkova, L.H. Andersen
    ChemPhysChem 2021, 22, 9, 833 — Cover article
  8. Bisguanidinium-Catalyzed Epoxidation of Allylic and Homoallylic Amines under Phase Transfer Conditions
    K.F. Chin, X. Ye, Y. Li, R. Lee, A.M. Kabylda, D. Leow, X. Zhang, E.C.X. Ang, C.-H. Tan
    ACS Catal. 2020, 10, 4, 2684 — Cover article
  9. Efficient Synthesis of the Peptide Fragment of the Natural Depsipeptides Jaspamide and Chondramide
    D.P. Zarezin, O.I. Shmatova, A.M. Kabylda, V.G. Nenajdenko
    Eur. J. Org. Chem. 2018, 34, 4716
  10. Efficient Synthesis of Tetrazole Derivatives of Cytisine Using the Azido-Ugi Reaction
    D.P. Zarezin, A.M. Kabylda, V.I. Vinogradova, P.V. Dorovatovskii, V.N. Khrustalev, V.G. Nenajdenko
    Tetrahedron 2018, 74, 32, 4315