| Molecular transformer: a model for uncertainty-calibrated chemical reaction prediction P Schwaller, T Laino, T Gaudin, P Bolgar, CA Hunter, C Bekas, AA Lee ACS central science 5 (9), 1572-1583, 2019 | 875 | 2019 |
| Identifying degradation patterns of lithium ion batteries from impedance spectroscopy using machine learning Y Zhang, Q Tang, Y Zhang, J Wang, U Stimming, AA Lee Nature Communications 11, 1706, 2020 | 781 | 2020 |
| The electrostatic screening length in concentrated electrolytes increases with concentration AM Smith, AA Lee, S Perkin The journal of physical chemistry letters 7 (12), 2157-2163, 2016 | 567 | 2016 |
| Long range electrostatic forces in ionic liquids MA Gebbie, AM Smith, HA Dobbs, GG Warr, X Banquy, M Valtiner, ... Chemical communications 53 (7), 1214-1224, 2017 | 376 | 2017 |
| Predicting materials properties without crystal structure: deep representation learning from stoichiometry REA Goodall, AA Lee Nature communications 11 (1), 6280, 2020 | 287 | 2020 |
| Scaling analysis of the screening length in concentrated electrolytes AA Lee, CS Perez-Martinez, AM Smith, S Perkin Physical review letters 119 (2), 026002, 2017 | 216 | 2017 |
| SARS-CoV-2 infects the human kidney and drives fibrosis in kidney organoids J Jansen, KC Reimer, JS Nagai, FS Varghese, GJ Overheul, M de Beer, ... Cell stem cell 29 (2), 217-231. e8, 2022 | 199 | 2022 |
| Underscreening in concentrated electrolyes AA Lee, C Perez-Martinez, AM Smith, S Perkin | 162* | 2017 |
| Underscreening in concentrated electrolytes AA Lee, C Perez-Martinez, AM Smith, S Perkin Faraday Discussions, 2017 | 157 | 2017 |
| Are room-temperature ionic liquids dilute electrolytes? AA Lee, D Vella, S Perkin, A Goriely The journal of physical chemistry letters 6 (1), 159-163, 2015 | 148 | 2015 |
| Bayesian semi-supervised learning for uncertainty-calibrated prediction of molecular properties and active learning Y Zhang Chemical science 10 (35), 8154-8163, 2019 | 143 | 2019 |
| Alternative radical pairs for cryptochrome-based magnetoreception AA Lee, JCS Lau, HJ Hogben, T Biskup, DR Kattnig, PJ Hore Journal of The Royal Society Interface 11 (95), 20131063, 2014 | 143 | 2014 |
| Learning the molecular grammar of protein condensates from sequence determinants and embeddings KL Saar, AS Morgunov, R Qi, WE Arter, G Krainer, AA Lee, TPJ Knowles Proceedings of the National Academy of Sciences 118 (15), e2019053118, 2021 | 136 | 2021 |
| Impedance-based forecasting of lithium-ion battery performance amid uneven usage PK Jones, U Stimming, AA Lee Nature Communications 13 (1), 4806, 2022 | 125 | 2022 |
| Molecular transformer unifies reaction prediction and retrosynthesis across pharma chemical space AA Lee, Q Yang, V Sresht, P Bolgar, X Hou, JL Klug-McLeod, CR Butler Chemical Communications 55 (81), 12152-12155, 2019 | 119 | 2019 |
| Crowdsourcing drug discovery for pandemics J Chodera, AA Lee, N London, F von Delft Nature Chemistry 12 (7), 581-581, 2020 | 109 | 2020 |
| Switching the structural force in ionic liquid-solvent mixtures by varying composition AM Smith, AA Lee, S Perkin Physical Review Letters 118 (9), 096002, 2017 | 89 | 2017 |
| Accelerating antiviral drug discovery: lessons from COVID-19 A von Delft, MD Hall, AD Kwong, LA Purcell, KS Saikatendu, U Schmitz, ... Nature Reviews Drug Discovery 22 (7), 585-603, 2023 | 80 | 2023 |
| Quantitative interpretation explains machine learning models for chemical reaction prediction and uncovers bias DP Kovács, W McCorkindale, AA Lee Nature communications 12 (1), 1695, 2021 | 77 | 2021 |
| Energy–entropy competition and the effectiveness of stochastic gradient descent in machine learning Y Zhang, AM Saxe, MS Advani, AA Lee Molecular Physics 116 (21-22), 3214-3223, 2018 | 76 | 2018 |