Zobrazeno 1 - 10
of 44
pro vyhledávání: '"James R. Kermode"'
Publikováno v:
npj Computational Materials, Vol 8, Iss 1, Pp 1-13 (2022)
Abstract Many atomic descriptors are currently limited by their unfavourable scaling with the number of chemical elements S e.g. the length of body-ordered descriptors, such as the SOAP power spectrum (3-body) and the (ACE) (multiple body-orders), sc
Externí odkaz:
https://doaj.org/article/5075014f14b3459d969e4f5dd11000dc
Autor:
Liwei Zhang, Berk Onat, Geneviève Dusson, Adam McSloy, G. Anand, Reinhard J. Maurer, Christoph Ortner, James R. Kermode
Publikováno v:
npj Computational Materials, Vol 8, Iss 1, Pp 1-14 (2022)
Abstract We propose a scheme to construct predictive models for Hamiltonian matrices in atomic orbital representation from ab initio data as a function of atomic and bond environments. The scheme goes beyond conventional tight binding descriptions as
Externí odkaz:
https://doaj.org/article/3365b927591f4ba9bc2bb5a7fedc1d90
Autor:
Giorgio Sernicola, Tommaso Giovannini, Punit Patel, James R. Kermode, Daniel S. Balint, T. Ben Britton, Finn Giuliani
Publikováno v:
Nature Communications, Vol 8, Iss 1, Pp 1-9 (2017)
To improve mechanical properties in ceramics through grain boundary engineering, precise mechanical characterization of individual boundaries is vital yet difficult to achieve. Here authors perform experiments using an in situ scanning electron micro
Externí odkaz:
https://doaj.org/article/5b04f69db942434bb28086bc735e1c9f
Publikováno v:
Machine Learning: Science and Technology, Vol 4, Iss 1, p 015020 (2023)
We present a data-parallel software package for fitting Gaussian approximation potentials (GAPs) on multiple nodes using the ScaLAPACK library with MPI and OpenMP. Until now the maximum training set size for GAP models has been limited by the availab
Externí odkaz:
https://doaj.org/article/9646075d7fe245e88d9be4e46e2012a2
Autor:
Petr Grigorev, Alexandra M. Goryaeva, Mihai-Cosmin Marinica, James R. Kermode, Thomas D. Swinburne
Publikováno v:
Acta Materialia
Acta Materialia, 2023, 247, pp.118734. ⟨10.1016/j.actamat.2023.118734⟩
Acta Materialia, 2023, 247, pp.118734. ⟨10.1016/j.actamat.2023.118734⟩
International audience; Calculations of dislocation-defect interactions are essential to model metallic strength, but the required system sizes are at or beyond ab initio limits. Current estimates thus have extrapolation or finite size errors that ar
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1594f3d51993a8f6d9f4d556d6d3c5f3
https://hal.science/hal-03993850/document
https://hal.science/hal-03993850/document
Autor:
G. Anand, Swarnava Ghosh, Liwei Zhang, Angesh Anupam, Colin L. Freeman, Christoph Ortner, Markus Eisenbach, James R. Kermode
Publikováno v:
Journal of The Institution of Engineers (India): Series D.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::28c56fe513d3cb862a9a2097e2b00ebb
https://doi.org/10.1007/s40033-022-00424-z
https://doi.org/10.1007/s40033-022-00424-z
Publikováno v:
ArXiv.org
We present a data-parallel software package for fitting Gaussian Approximation Potentials (GAPs) on multiple nodes using the ScaLAPACK library with MPI and OpenMP. Until now the maximum training set size for GAP models has been limited by the availab
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::bf7845fd33322f2d58d970026fa043a1
http://arxiv.org/abs/2207.03803
http://arxiv.org/abs/2207.03803
Autor:
Thomas D. Swinburne, Alexandra Goryaeva, Jacopo Baima, Clovis Lapointe, Lisa Ventelon, Julien Dérès, Petr Grigorev, Mihai-Cosmin Marinica, James R. Kermode
Publikováno v:
Physical Review Materials
Physical Review Materials, 2021, 5 (10), pp.103803. ⟨10.1103/PhysRevMaterials.5.103803⟩
Physical Review Materials, American Physical Society, 2021, 5 (10), pp.103803. ⟨10.1103/PhysRevMaterials.5.103803⟩
Physical Review Materials, 2021, 5 (10), pp.103803. ⟨10.1103/PhysRevMaterials.5.103803⟩
Physical Review Materials, American Physical Society, 2021, 5 (10), pp.103803. ⟨10.1103/PhysRevMaterials.5.103803⟩
Data-driven, or machine learning (ML), approaches have become viable alternatives to semiempirical methods to construct interatomic potentials, due to their capacity to accurately interpolate and extrapolate from first-principles simulations if the t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::bf68d3c9c935f26c6efb49c566fe0726
https://hal.science/hal-03395915/document
https://hal.science/hal-03395915/document
Autor:
Liwei Zhang, Berk Onat, Geneviève Dusson, Adam McSloy, G. Anand, Reinhard J. Maurer, Christoph Ortner, James R. Kermode
Publikováno v:
npj Comp. Mater.
We propose a scheme to construct predictive models for Hamiltonian matrices in atomic orbital representation from ab initio data as a function of atomic and bond environments. The scheme goes beyond conventional tight binding descriptions as it repre
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::625fa562a8e1ed61199ac52172d8e6aa
We investigate the failure of carbon-nanotube/polymer composites by using a recently-developed hybrid quantum-mechanical/molecular-mechanical (QM/MM) approach to simulate nanotube pull-out from a cross-linked polyethene matrix. Our study focuses on t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3620ef8f00b174125bff516482c766fe
http://wrap.warwick.ac.uk/137320/1/WRAP-Atomistic-QM-MM-simulations-strength-polymer-composites-Kermode-2020.pdf
http://wrap.warwick.ac.uk/137320/1/WRAP-Atomistic-QM-MM-simulations-strength-polymer-composites-Kermode-2020.pdf