| Regio-selectivity prediction with a machine-learned reaction representation and on-the-fly quantum mechanical descriptors Y Guan, CW Coley, H Wu, D Ranasinghe, E Heid, TJ Struble, L Pattanaik, ... Chemical Science 12 (6), 2198-2208, 2021 | 101 | 2021 |
| Machine learning of reaction properties via learned representations of the condensed graph of reaction E Heid, WH Green Journal of Chemical Information and Modeling 62 (9), 2101-2110, 2021 | 68 | 2021 |
| Quantum mechanical determination of atomic polarizabilities of ionic liquids E Heid, A Szabadi, C Schröder Physical Chemistry Chemical Physics 20 (16), 10992-10996, 2018 | 67 | 2018 |
| Additive polarizabilities in ionic liquids CES Bernardes, K Shimizu, JNC Lopes, P Marquetand, E Heid, ... Physical Chemistry Chemical Physics 18 (3), 1665-1670, 2016 | 45 | 2016 |
| Chemprop: A machine learning package for chemical property prediction E Heid, KP Greenman, Y Chung, SC Li, DE Graff, FH Vermeire, H Wu, ... Journal of Chemical Information and Modeling 64 (1), 9-17, 2023 | 37 | 2023 |
| 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, ... Journal of hazardous materials 340, 113-119, 2017 | 36 | 2017 |
| 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, ... The journal of physical chemistry letters 11 (6), 2165-2170, 2020 | 29 | 2020 |
| The physical significance of the Kamlet–Taft π* parameter of ionic liquids N Weiß, CH Schmidt, G Thielemann, E Heid, C Schröder, S Spange Physical Chemistry Chemical Physics 23 (2), 1616-1626, 2021 | 27 | 2021 |
| Toward prediction of electrostatic parameters for force fields that explicitly treat electronic polarization E Heid, M Fleck, P Chatterjee, C Schröder, AD MacKerell Jr Journal of chemical theory and computation 15 (4), 2460-2469, 2019 | 27 | 2019 |
| Machine-learning-guided discovery of electrochemical reactions AF Zahrt, Y Mo, KY Nandiwale, R Shprints, E Heid, KF Jensen Journal of the American Chemical Society 144 (49), 22599-22610, 2022 | 24 | 2022 |
| Solvation dynamics in polar solvents and imidazolium ionic liquids: failure of linear response approximations E Heid, C Schröder Physical Chemistry Chemical Physics 20 (7), 5246-5255, 2018 | 24 | 2018 |
| Evaluating excited state atomic polarizabilities of chromophores E Heid, PA Hunt, C Schröder Physical Chemistry Chemical Physics 20 (13), 8554-8563, 2018 | 24 | 2018 |
| Langevin behavior of the dielectric decrement in ionic liquid water mixtures E Heid, B Docampo-Álvarez, LM Varela, K Prosenz, O Steinhauser, ... Physical Chemistry Chemical Physics 20 (22), 15106-15117, 2018 | 22 | 2018 |
| Polarizable molecular dynamics simulations of ionic liquids: Influence of temperature control E Heid, S Boresch, C Schröder The Journal of chemical physics 152 (9), 2020 | 20 | 2020 |
| Additive polarizabilities of halides in ionic liquids and organic solvents E Heid, M Heindl, P Dienstl, C Schröder The Journal of Chemical Physics 149 (4), 2018 | 19 | 2018 |
| 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 The Journal of Chemical Physics 145 (16), 2016 | 18 | 2016 |
| Characterizing uncertainty in machine learning for chemistry E Heid, CJ McGill, FH Vermeire, WH Green Journal of Chemical Information and Modeling 63 (13), 4012–4029, 2023 | 17 | 2023 |
| On the validity of linear response approximations regarding the solvation dynamics of polyatomic solutes E Heid, W Moser, C Schröder Physical Chemistry Chemical Physics 19 (17), 10940-10950, 2017 | 17 | 2017 |
| On the value of using 3D shape and electrostatic similarities in deep generative methods G Bolcato, E Heid, J Boström Journal of Chemical Information and Modeling 62 (6), 1388-1398, 2022 | 16 | 2022 |
| Deep ensembles vs committees for uncertainty estimation in neural-network force fields: Comparison and application to active learning J Carrete, H Montes-Campos, R Wanzenböck, E Heid, GKH Madsen The Journal of Chemical Physics 158 (20), 204801, 2023 | 15 | 2023 |
