Zobrazeno 1 - 10
of 409
pro vyhledávání: '"Niblett, P"'
Autor:
Ponomarenko, L. A., Principi, Alessandro, Niblett, A. D., Wang, Wendong, Gorbachev, R. V., Kumaravadive, Piranavan, Berdyugin, A. I., Ermakov, A. V., Slizovskiy, Sergey, Watanabe, Kenji, Taniguchi, Takashi, Ge, Qi, Fal'ko, V. I., Eaves, Laurence, Greenaway, M. T., Geim, A. K.
Coulomb drag between adjacent electron and hole gases has attracted considerable attention, being studied in various two-dimensional systems, including semiconductor and graphene heterostructures. Here we report measurements of electron-hole drag in
Externí odkaz:
http://arxiv.org/abs/2410.10640
With the emergence of Foundational Machine Learning Interatomic Potential (FMLIP) models trained on extensive datasets, transferring data between different ML architectures has become increasingly important. In this work, we examine the extent to whi
Externí odkaz:
http://arxiv.org/abs/2409.05590
Autor:
Batatia, Ilyes, Benner, Philipp, Chiang, Yuan, Elena, Alin M., Kovács, Dávid P., Riebesell, Janosh, Advincula, Xavier R., Asta, Mark, Avaylon, Matthew, Baldwin, William J., Berger, Fabian, Bernstein, Noam, Bhowmik, Arghya, Blau, Samuel M., Cărare, Vlad, Darby, James P., De, Sandip, Della Pia, Flaviano, Deringer, Volker L., Elijošius, Rokas, El-Machachi, Zakariya, Falcioni, Fabio, Fako, Edvin, Ferrari, Andrea C., Genreith-Schriever, Annalena, George, Janine, Goodall, Rhys E. A., Grey, Clare P., Grigorev, Petr, Han, Shuang, Handley, Will, Heenen, Hendrik H., Hermansson, Kersti, Holm, Christian, Jaafar, Jad, Hofmann, Stephan, Jakob, Konstantin S., Jung, Hyunwook, Kapil, Venkat, Kaplan, Aaron D., Karimitari, Nima, Kermode, James R., Kroupa, Namu, Kullgren, Jolla, Kuner, Matthew C., Kuryla, Domantas, Liepuoniute, Guoda, Margraf, Johannes T., Magdău, Ioan-Bogdan, Michaelides, Angelos, Moore, J. Harry, Naik, Aakash A., Niblett, Samuel P., Norwood, Sam Walton, O'Neill, Niamh, Ortner, Christoph, Persson, Kristin A., Reuter, Karsten, Rosen, Andrew S., Schaaf, Lars L., Schran, Christoph, Shi, Benjamin X., Sivonxay, Eric, Stenczel, Tamás K., Svahn, Viktor, Sutton, Christopher, Swinburne, Thomas D., Tilly, Jules, van der Oord, Cas, Varga-Umbrich, Eszter, Vegge, Tejs, Vondrák, Martin, Wang, Yangshuai, Witt, William C., Zills, Fabian, Csányi, Gábor
Machine-learned force fields have transformed the atomistic modelling of materials by enabling simulations of ab initio quality on unprecedented time and length scales. However, they are currently limited by: (i) the significant computational and hum
Externí odkaz:
http://arxiv.org/abs/2401.00096
Autor:
Leonid A. Ponomarenko, Alessandro Principi, Andy D. Niblett, Wendong Wang, Roman V. Gorbachev, Piranavan Kumaravadivel, Alexey I. Berdyugin, Alexey V. Ermakov, Sergey Slizovskiy, Kenji Watanabe, Takashi Taniguchi, Qi Ge, Vladimir I. Fal’ko, Laurence Eaves, Mark T. Greenaway, Andre K. Geim
Publikováno v:
Nature Communications, Vol 15, Iss 1, Pp 1-6 (2024)
Abstract Coulomb drag between adjacent electron and hole gases has attracted considerable attention, being studied in various two-dimensional systems, including semiconductor and graphene heterostructures. Here we report measurements of electron–ho
Externí odkaz:
https://doaj.org/article/f05b3634bff7489f8fc2f2195dd915e7
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-19 (2024)
Abstract This study utilises computational fluid dynamics simulations with the OpenFOAM computational framework to investigate and compare the in-plane and through-plane permeability properties of four different gas diffusion layers (GDLs). Also the
Externí odkaz:
https://doaj.org/article/25bb45d2d3ae48af8a2627dfe5b7f487
Autor:
Zhou, Zhilei, Qiu, Ziyu, Niblett, Brad, Johnston, Andrew, Schwartzentruber, Jeffrey, Zincir-Heywood, Nur, Heywood, Malcolm
Publikováno v:
LNCS, 29 March 2023
An approach to evolutionary ensemble learning for classification is proposed in which boosting is used to construct a stack of programs. Each application of boosting identifies a single champion and a residual dataset, i.e. the training records that
Externí odkaz:
http://arxiv.org/abs/2211.15621
Autor:
Adrian Mularczyk, Daniel Niblett, Adam Wijpkema, Marc P. F. H. L. vanMaris, Antoni Forner‐Cuenca
Publikováno v:
Advanced Materials Interfaces, Vol 11, Iss 16, Pp n/a-n/a (2024)
Abstract The 3D structure (i.e., microstructure) of porous electrodes governs the performance of emerging electrochemical technologies such as fuel cells, electrolysis, and batteries. Sustaining electrochemical reactions and convective‐diffusive ma
Externí odkaz:
https://doaj.org/article/08d53a5ff27741ceaad4a4dd331b2efd
By adopting a perspective informed by contemporary liquid state theory, we consider how to train an artificial neural network potential to describe inhomogeneous, disordered systems. We find that neural network potentials based on local representatio
Externí odkaz:
http://arxiv.org/abs/2107.06208
Autor:
Niblett, Samuel P., Limmer, David T.
Using molecular dynamics simulations and methods of importance sampling, we study the thermodynamics and dynamics of sodium chloride in the aqueous premelting layer formed spontaneously at the interface between ice and its vapor. We uncover a hierarc
Externí odkaz:
http://arxiv.org/abs/2012.09881
Autor:
Nia Roberts, Reem Saleem Malouf, Anya Topiwala, Angeline Lee, Suraj Shah, Vidushi Pradhan, Karyna Atha, Peter Indoe, Naira Mahmoud, Guy Niblett
Publikováno v:
BMJ Open, Vol 14, Iss 2 (2024)
Objectives Preservation of brain health is an urgent priority for the world’s ageing population. The evidence base for brain health optimisation strategies is rapidly expanding, but clear recommendations have been limited by heterogeneity in measur
Externí odkaz:
https://doaj.org/article/0877fbd8771d4bfab7019b1b603e9c95