| Development of a machine learning potential for graphene P Rowe, G Csányi, D Alfe, A Michaelides Physical Review B 97 (5), 054303, 2018 | 201 | 2018 |
| An accurate and transferable machine learning potential for carbon P Rowe, VL Deringer, P Gasparotto, G Csányi, A Michaelides The Journal of Chemical Physics 153 (3), 2020 | 188 | 2020 |
| Machine learning potentials for complex aqueous systems made simple C Schran, FL Thiemann, P Rowe, EA Müller, O Marsalek, A Michaelides Proceedings of the National Academy of Sciences 118 (38), e2110077118, 2021 | 115 | 2021 |
| Cation-controlled wetting properties of vermiculite membranes and its promise for fouling resistant oil–water separation K Huang, P Rowe, C Chi, V Sreepal, T Bohn, KG Zhou, Y Su, E Prestat, ... Nature communications 11 (1), 1097, 2020 | 100 | 2020 |
| Water flow in single-wall nanotubes: Oxygen makes it slip, hydrogen makes it stick FL Thiemann, C Schran, P Rowe, EA Müller, A Michaelides ACS nano 16 (7), 10775-10782, 2022 | 33 | 2022 |
| Machine learning potential for hexagonal boron nitride applied to thermally and mechanically induced rippling FL Thiemann, P Rowe, EA Müller, A Michaelides The Journal of Physical Chemistry C 124 (40), 22278-22290, 2020 | 33 | 2020 |
| Importance and nature of short-range excitonic interactions in light harvesting complexes and organic semiconductors RP Fornari, P Rowe, D Padula, A Troisi Journal of Chemical Theory and Computation 13 (8), 3754-3763, 2017 | 30 | 2017 |
| pH-dependent water permeability switching and its memory in MoS2 membranes CY Hu, A Achari, P Rowe, H Xiao, S Suran, Z Li, K Huang, C Chi, ... Nature 616 (7958), 719-723, 2023 | 29 | 2023 |
| Defect-dependent corrugation in graphene FL Thiemann, P Rowe, A Zen, EA Muller, A Michaelides Nano Letters 21 (19), 8143-8150, 2021 | 28 | 2021 |
| Structure-Dynamics Relation in Physically-Plausible MultiChromophore Systems AD George C. Knee, Patrick Rowe, Luke D. Smith, Alessandro Troisi Journal of Physical Chemistry Letters 8, 2328, 2017 | 18 | 2017 |
| Accelerating the prediction of large carbon clusters via structure search: Evaluation of machine-learning and classical potentials B Karasulu, JM Leyssale, P Rowe, C Weber, C de Tomas Carbon 191, 255-266, 2022 | 12 | 2022 |
| Functional and specific T-cell engagers against a peptide-MHC tumor target D Tortora, P Bergqvist, A Goodman, R Blackler, N Blamey, S Carrara, ... Cancer Research 84 (6_Supplement), 2373-2373, 2024 | | 2024 |
| 1395 Targeting intracellular tumor antigens to Figureht cancer: discovery and development of functional and specific T-cell engagers against a MAGE-A4 pMHC D Tortora, P Bergqvist, T Jacobs, P Farber, R Blackler, A Samiotakis, ... Journal for ImmunoTherapy of Cancer 11 (Suppl 1), 2023 | | 2023 |
| 1367 A rational approach to selecting CD3-binding antibodies for T-cell engager development JM Mai, V de Puyraimond, P Pouliot, K Caldwell, L Clifford, A Goodman, ... Journal for ImmunoTherapy of Cancer 11 (Suppl 1), 2023 | | 2023 |
| Accuracy and Transferability in Machine Learned Potentials for Carbon P Rowe UCL (University College London), 2021 | | 2021 |
| Machine learning potentials for complex aqueous systems made simple E Muller, C Schran, F Thiemann, P Rowe, O Marsalek, A Michaelides National Academy of Sciences, 0 | | |
| Making sense of charge and exciton dynamics in organic materials via model reduction A Troisi, D Padula, K Claridge, M Lee, R Fornari, P Rowe | | |
