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Publications

  1. 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. submitted (2025)
  2. 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. submitted (2025), Preprint
  3. 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
  4. 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
  5. 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
  6. 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
  7. 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
  8. 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
  9. 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
  10. 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
  11. 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
  12. 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
  13. 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
  14. 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
  15. 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
  16. 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
  17. 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
  18. 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
  19. 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
  20. 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
  21. 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
  22. 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
  23. 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
  24. 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
  25. 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
  26. 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
  27. 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
  28. 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
  29. 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
  30. 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
  31. 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
  32. 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
  33. 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
  34. 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
  35. 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
  36. 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
  37. 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
  38. Computational solvation dynamics of oxyquinolinium betaine linked to trehalose. E. Heid, C. Schröder. J. Chem. Phys. (2016) 145, 164507, DOI:10.1063/1.4966189
  39. 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
  40. 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

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