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Publications

  1. Feynman-Kac-Flow: Inference Steering of Conditional Flow Matching to an Energy-Tilted Posterior. K. Mark, L. Galustian, M. P.-P. Kovar, E. Heid. (2025) Preprint
  2. ChemTorch: A Deep Learning Framework for Benchmarking and Developing Chemical Reaction Property Prediction Models. J. De Landsheere, A. Zamyatin, J. Karwounopoulos, E. Heid. (2025) Preprint
  3. Graph-based prediction of reaction barrier heights with on-the-fly prediction of transition states. J. Karwounopoulos, J. De Landsheere, L. Galustian, T. Jechtl, E. Heid. Dig. Discov. (2025). DOI:10.1039/D5DD00240K
  4. GoFlow: Efficient Transition State Geometry Prediction with Flow Matching and E(3)-Equivariant Neural Networks. L. Galustian, K. Mark, J. Karwounopoulos, M. P.-P. Kovar, E. Heid. (2025) Preprint
  5. The density isobar of water: A comparative study of vdW-DF-cx and RPBE-D3. N. Bučková, N. Unglert, J. Schörghuber, E. Heid, K. Berland, G. K. H. Madsen. J. Chem. Phys. (2025), 163, 104102 DOI:10.1063/5.0278026
  6. From flat to stepped: Active learning frameworks for investigating local structure at copper-water interfaces. J. Schörghuber, N. Bučková, E. Heid, G. K. H. Madsen. Phys. Chem. Chem. Phys. (2025), 27, 9169 DOI:10.1039/D5CP00396B
  7. A comprehensive approach to incorporating intermolecular dispersion into the openCOSMO-RS model. Part 2: Atomic polarizabilities. D. Grigorash, S. Müller, E. Heid, F. Neese, D. Liakos, C. Riplinger, M. García-Ratés, P. Paricaud, E. H. Stenby, I. Smirnova, W. Yan. Chem. Eng. Sci. (2025), 319, 122170 DOI:10.1016/j.ces.2025.122170
  8. Exploring inhomogeneous surfaces: Ti-rich SrTiO3(110) reconstructions via active learning. R. Wanzenböck, E. Heid, M. Riva, G. Franceschi, A. M. Imre, J. Carrete, U. Diebold, G. K. H. Madsen. Dig. Discov. (2024), 3, 2137-2145. DOI:10.1039/D4DD00231H
  9. Spatially resolved uncertainties for machine learning potentials. E. Heid, J. Schörghuber, R. Wanzenböck, G. K. H. Madsen. J. Chem. Inf. Model. (2024), 64, 6377-6387 DOI:10.1021/acs.jcim.4c00904
  10. LoGAN: Local generative adversarial network for novel structure prediction. P. Kovacs, E. Heid, G. K. H. Madsen. Mach. Learn.: Sci. Technol. (2024), 5, 035079 DOI:10.1088/2632-2153/ad7a4d
  11. Chemprop: A Machine Learning Package for Chemical Property Prediction. E. Heid, K. P. Greenman, Y. Chung, S.-C. Li, D. E. Graff, F. H. Vermeire, H. Wu, W. H. Green, C. J. McGill. J. Chem. Inf. Model. (2023), 64, 9-17 DOI:10.1021/acs.jcim.3c01250
  12. EnzymeMap: Curation, validation and data-driven prediction of enzymatic reactions. E. Heid, D. Probst, W. H. Green, G. K. H. Madsen. Chem. Sci. (2023), 14, 14229-14242. DOI:10.1039/D3SC02048G
  13. Deep Ensembles vs. Committees for Uncertainty Estimation in Neural-Network Force Fields: Comparison and Application to Active Learning. J. Carrete, J. Montes-Campos, R. Wanzenböck, E. Heid, G. K. H. Madsen. J. Chem. Phys. (2023), 158, 204801 DOI:10.1063/5.0146905
  14. Characterizing Uncertainty in Machine Learning for Chemistry. E. Heid, C. J. McGill, F. Vermeire, W. H. Green. J. Chem. Inf. Model. (2023), 63, 4012-4029 DOI:10.1021/acs.jcim.3c00373
  15. Machine Learning-Guided Discovery of New Electrochemical Reactions. A. Zahrt, Y. Mo, K. Nandiwale, R. Shprints, E. Heid, K. F. Jensen. J. Am. Chem. Soc. (2022), 144, 22599-22610 DOI:10.1021/jacs.2c08997
  16. On the Value of Using 3D Shape and Electrostatic Similarities in Deep Generative Methods. G. Bolcato, E. Heid, J. Boström. J. Chem. Inf. Model. (2022) 62, 1388-1398, DOI:10.1021/acs.jcim.1c01535
  17. Collectivity in ionic liquids: a temperature dependent, polarizable molecular dynamics study. A. Szabadi, P. Honegger, F. Schöfbeck, M. Sappl, E. Heid, O. Steinhauser, C. Schröder. Phys. Chem. Chem. Phys. (2022) 24, 15776-15790, DOI:10.1039/D2CP00898J
  18. Similarity based enzymatic retrosynthesis. K. Sankaranarayanan, E. Heid, C. W. Coley, D. Verma, W. H. Green, K. F. Jensen. Chem. Sci. (2022) 13, 6039-6053, DOI:10.1039/D2SC01588A
  19. Machine learning of reaction properties via learned representations of the condensed graph of reaction. E. Heid, W. H. Green. J. Chem. Inf. Model. (2022) 62, 2101-2110, DOI:10.1021/acs.jcim.1c00975
  20. Influence of Template Size, Canonicalization, and Exclusivity for Retrosynthesis and Reaction Prediction Applications. E. Heid, J. Liu, A. Aude, W. H. Green. J. Chem. Inf. Model. (2021) 62, 16-26, DOI:10.1021/acs.jcim.1c01192
  21. EHreact: Extended Hasse diagrams for the extraction and scoring of enzymatic reaction templates. E. Heid, S. Goldman, K. Sankaranarayaran, C. W. Coley, C. Flamm, W. H. Green. J. Chem. Inf. Model (2021) 61, 4949-4061, DOI:10.1021/acs.jcim.1c00921
  22. Solvation of anthraquinone and TEMPO redox-active species in acetonitrile using a polarizable force field. R. Berthin, A. Serva, K. G. Reeves, E. Heid, C. Schröder, M. Salanne. J. Chem. Phys. (2021) 155, 074504, DOI:10.1063/5.0061891
  23. Regio-selectivity prediction with a machine-learned reaction representation and on-the-fly quantum mechanical descriptors. Y. Guan, C. W. Coley, H. Wu, D. Ranasinghe, E. Heid, T. J. Struble, L. Pattanaik, W. H. Green, K. F. Jensen. Chem. Sci. (2021) 12, 2198-2208, DOI:10.1039/D0SC04823B
  24. The physical significance of the Kamlet–Taft π* parameter of ionic liquids. N. Weiß, C. H Schmidt, G. Thielemann, E. Heid, C. Schröder, S. Spange. Phys. Chem. Chem. Phys. (2021) 23, 1616-1626, DOI:10.1039/D0CP04989A
  25. Polarizable molecular dynamics simulations of ionic liquids: Influence of temperature control. E. Heid, S. Boresch, C. Schröder. J. Chem. Phys. (2020) 152, 094105, DOI:10.1063/1.5143746
  26. Understanding the nature of nuclear magnetic resonance relaxation by means of fast-field-cycling relaxometry and molecular dynamics simulations—the validity of relaxation models. P. Honegger, V. Overbeck, A. Strate, A. Appelhagen, M. Sappl, E. Heid, C. Schröder, R. Ludwig, O. Steinhauser. J. Phys. Chem. Lett. (2020) 11, 2165-2170, DOI:10.1021/acs.jpclett.0c00087
  27. Dielectric spectroscopy and time dependent Stokes shift: two faces of the same coin? P. Honegger, E. Heid, C. Schröder, O. Steinhauser. Phys. Chem. Chem. Phys. (2020) 22, 18388-18399, DOI:10.1039/D0CP02840A
  28. Computational solvation dynamics: Implementation, application, and validation. C. Schröder, E. Heid. Annu. Rep. Comput. Chem. (2020) 16, 93-154, DOI:10.1016/bs.arcc.2020.07.001
  29. Computational spectroscopy of trehalose, sucrose, maltose, and glucose: A comprehensive study of TDSS, NQR, NOE, and DRS. E. Heid, P. Honegger, D. Braun, A. Szabadi, T. Stankovic, O. Steinhauser, C. Schröder. J. Chem. Phys. (2019) 150, 175102, DOI:10.1063/1.5095058
  30. Toward prediction of electrostatic parameters for force fields that explicitly treat electronic polarization. E. Heid, M. Fleck, P. Chatterjee, C. Schröder, A. D. MacKerell Jr. J. Chem. Theory Comput. (2019) 15, 2460-2469, DOI:10.1021/acs.jctc.8b01289
  31. Changes in protein hydration dynamics by encapsulation or crowding of ubiquitin: strong correlation between time-dependent Stokes shift and intermolecular nuclear Overhauser effect. P. Honegger, E. Heid, S. Schmode, C. Schröder, O. Steinhauser. RSC Adv. (2019) 9, 36982-36993, DOI:10.1039/C9RA08008B
  32. Solvation dynamics: improved reproduction of the time-dependent Stokes shift with polarizable empirical force field chromophore models. E. Heid, S. Schmode, P. Chatterjee, A. D. MacKerell Jr, C. Schröder. Phys. Chem. Chem. Phys. (2019) 21, 17703-17710, DOI:10.1039/C9CP03000J
  33. Fundamental limitations of the time-dependent Stokes shift for investigating protein hydration dynamics. E. Heid, D. Braun. Phys. Chem. Chem. Phys. (2019) 21, 4435-4443, DOI:10.1039/C8CP07623E
  34. Polarizability in ionic liquid simulations causes hidden breakdown of linear response theory. E. Heid, C. Schröder. Phys. Chem. Chem. Phys. (2019) 21, 1023-1028, DOI:10.1039/C8CP06569A
  35. Additive polarizabilities of halides in ionic liquids and organic solvents. E. Heid, M. Heindl, P. Dienstl, C. Schröder. J. Chem. Phys. (2018) 149, 044302, DOI:10.1063/1.5043156
  36. Langevin behavior of the dielectric decrement in ionic liquid water mixtures. E. Heid, B. Docampo-Alvarez, L. M. Varela, K. Prosenz, O. Steinhauser, C. Schröder. Phys. Chem. Chem. Phys. (2018) 20, 15106-15117, DOI:10.1039/C8CP02111B
  37. Solvation dynamics in polar solvents and imidazolium ionic liquids: failure of linear response approximations. E. Heid, C. Schröder. Phys. Chem. Chem. Phys. (2018) 20, 5246-5255, DOI:10.1039/C7CP07052G
  38. Quantum mechanical determination of atomic polarizabilities of ionic liquids. E. Heid, A. Szabadi, C. Schröder. Phys. Chem. Chem. Phys. (2018) 20, 10992-10996, DOI:10.1039/C8CP01677A
  39. Evaluating excited state atomic polarizabilities of chromophores. E. Heid, P. A. Hunt, C. Schröder. Phys. Chem. Chem. Phys. (2018) 20, 8554-8563, DOI:10.1039/C7CP08549D
  40. Effect of a tertiary butyl group on polar solvation dynamics in aqueous solution: a computational approach. E. Heid, C. Schröder. J. Phys. Chem. B (2017) 121, 9639-9646, DOI:10.1021/acs.jpcb.7b05039
  41. Thioglycolate-based task-specific ionic liquids: Metal extraction abilities vs acute algal toxicity. S. Platzer, R. Leyma, S. Wolske, W. Kandioller, E. Heid, C. Schröder, M. Schagerl, R. Krachler, F. Jirsa, B. K. Keppler. J. Hazard. Mater. (2017) 340, 113-119, DOI:10.1016/j.jhazmat.2017.06.053
  42. On the validity of linear response approximations regarding the solvation dynamics of polyatomic solutes. E. Heid, W. Moser, C. Schröder. Phys. Chem. Chem. Phys. (2017) 19, 10940-10950, DOI:10.1039/C6CP08575J
  43. Computational solvation dynamics of oxyquinolinium betaine linked to trehalose. E. Heid, C. Schröder. J. Chem. Phys. (2016) 145, 164507, DOI:10.1063/1.4966189
  44. The small impact of various partial charge distributions in ground and excited state on the computational Stokes shift of 1-methyl-6-oxyquinolinium betaine in diverse water models. E. Heid, S. Harringer, C. Schröder. J. Chem. Phys. (2016) 145, 164506, DOI:10.1063/1.4966147
  45. Additive polarizabilities in ionic liquids. C. E. S. Bernardes, K. Shimizu, J. N. C. Lopes, P. Marquetand, E. Heid, O. Steinhauser, C. Schröder. Phys. Chem. Chem. Phys. (2016) 18, 1665-1670, DOI:10.1039/C5CP06595J

(c) Copyright 2025 Esther Heid