Christoph Schran

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Angelos MichaelidesUniversity of CambridgeDirección de correo verificada de cam.ac.uk
Fabien BrieucCEADirección de correo verificada de cea.fr
Venkat KapilLecturer (Assistant Professor), Department of Physics & Astronomy, University College LondonDirección de correo verificada de ucl.ac.uk
Ondrej MarsalekFaculty of Mathematics and Physics, Charles UniversityDirección de correo verificada de mff.cuni.cz
Fabian ThiemannIBM ResearchDirección de correo verificada de ibm.com
Erich A. MullerProfessor of Thermodynamics; Imperial College LondonDirección de correo verificada de imperial.ac.uk
Patrick RoweDirección de correo verificada de cam.ac.uk
Jörg BehlerRuhr-Universität BochumDirección de correo verificada de ruhr-uni-bochum.de
Kara D. FongCalifornia Institute of TechnologyDirección de correo verificada de caltech.edu
Niamh O'NeillPhD student, University of CambridgeDirección de correo verificada de cam.ac.uk
Chris J. PickardSir Alan Cottrell Professor of Materials Science, University of CambridgeDirección de correo verificada de cam.ac.uk
Richard BeckmannPostdoctoral Researcher, Chalmers Technical UniversityDirección de correo verificada de rub.de
Chang Woo MyungSungkyunkwan UniversityDirección de correo verificada de skku.edu
Thomas MarklandAssociate Professor of Chemistry, Stanford UniversityDirección de correo verificada de stanford.edu
Xavier R. AdvinculaUniversity of CambridgeDirección de correo verificada de cam.ac.uk
Stephen J. CoxAssistant Professor and Royal Society University Research Fellow, Durham UniversityDirección de correo verificada de durham.ac.uk
Samuel BrookesPhD Student - Cambridge University.Dirección de correo verificada de cam.ac.uk

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Christoph Schran

Assistant Professor, Department of Physics, University of Cambridge

Dirección de correo verificada de cam.ac.uk - Página principal

Molecular Dynamics Machine Learning Potentials Hydrogen Bonding Water


Título Ordenar por citas Ordenar por año Ordenar por título	Citado por Citado por	Año
The first-principles phase diagram of monolayer nanoconfined water V Kapil, C Schran, A Zen, J Chen, CJ Pickard, A Michaelides Nature 609, 512–516, 2022	185	2022
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	165	2021
A foundation model for atomistic materials chemistry I Batatia, P Benner, Y Chiang, AM Elena, DP Kovács, J Riebesell, ... arXiv preprint arXiv:2401.00096, 2023	151	2023
Committee neural network potentials control generalization errors and enable active learning C Schran, K Brezina, O Marsalek The Journal of chemical physics 153 (10), 104105, 2020	151	2020
Automated Fitting of Neural Network Potentials at Coupled Cluster Accuracy: Protonated Water Clusters as Testing Ground C Schran, J Behler, D Marx Journal of chemical theory and computation 16 (1), 88-99, 2020	120	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	58	2022
Tracking single adatoms in liquid in a transmission electron microscope N Clark, DJ Kelly, M Zhou, YC Zou, CW Myung, DG Hopkinson, C Schran, ... Nature 609 (7929), 942-947, 2022	51	2022
High-dimensional neural network potentials for solvation: The case of protonated water clusters in helium C Schran, F Uhl, J Behler, D Marx The Journal of Chemical Physics 148 (10), 2018	41	2018
Unravelling the influence of quantum proton delocalization on electronic charge transfer through the hydrogen bond C Schran, O Marsalek, TE Markland Chemical Physics Letters 678, 289-295, 2017	37	2017
Converged Colored Noise Path Integral Molecular Dynamics Study of the Zundel Cation Down to Ultralow Temperatures at Coupled Cluster Accuracy C Schran, F Brieuc, D Marx Journal of chemical theory and computation 14 (10), 5068-5078, 2018	36	2018
Correlated particle motion and THz spectral response of supercritical water M Śmiechowski, C Schran, H Forbert, D Marx Physical review letters 116 (2), 027801, 2016	33	2016
Transferability of machine learning potentials: Protonated water neural network potential applied to the protonated water hexamer C Schran, F Brieuc, D Marx The Journal of Chemical Physics 154 (5), 2021	29	2021
Converged quantum simulations of reactive solutes in superfluid helium: The Bochum perspective F Brieuc, C Schran, F Uhl, H Forbert, D Marx The Journal of Chemical Physics 152 (21), 2020	27	2020
Quantum nature of the hydrogen bond from ambient conditions down to ultra-low temperatures C Schran, D Marx Physical Chemistry Chemical Physics 21 (45), 24967-24975, 2019	25	2019
Infrared spectra at coupled cluster accuracy from neural network representations R Beckmann, F Brieuc, C Schran, D Marx Journal of Chemical Theory and Computation 18 (9), 5492-5501, 2022	22	2022
Force‐Induced Catastrophes on Energy Landscapes: Mechanochemical Manipulation of Downhill and Uphill Bifurcations Explains the Ring‐Opening Selectivity of Cyclopropanes M Wollenhaupt, C Schran, M Krupička, D Marx ChemPhysChem 19 (7), 837-847, 2018	20	2018
Origin of dielectric polarization suppression in confined water from first principles T Dufils, C Schran, J Chen, AK Geim, L Fumagalli, A Michaelides Chemical Science 15 (2), 516-527, 2024	19*	2024
State-resolved infrared spectrum of the protonated water dimer: Revisiting the characteristic proton transfer doublet peak HR Larsson, M Schröder, R Beckmann, F Brieuc, C Schran, D Marx, ... Chemical Science 13 (37), 11119-11125, 2022	17	2022
To pair or not to pair? Machine-learned explicitly-correlated electronic structure for NaCl in water N O’Neill, BX Shi, K Fong, A Michaelides, C Schran The Journal of Physical Chemistry Letters 15, 6081-6091, 2024	13	2024
The interplay of solvation and polarization effects on ion pairing in nanoconfined electrolytes KD Fong, B Sumic, N O’Neill, C Schran, CP Grey, A Michaelides Nano Letters 24 (16), 5024-5030, 2024	12	2024

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