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Patrick Rowe
Patrick Rowe
Afiliação desconhecida
E-mail confirmado em cam.ac.uk
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Citado por
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Ano
Development of a machine learning potential for graphene
P Rowe, G Csányi, D Alfe, A Michaelides
Physical Review B 97 (5), 054303, 2018
1982018
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
1822020
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
1112021
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
992020
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
322020
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
312022
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
302017
Defect-dependent corrugation in graphene
FL Thiemann, P Rowe, A Zen, EA Muller, A Michaelides
Nano Letters 21 (19), 8143-8150, 2021
282021
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
222023
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
182017
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
122022
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
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