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您的检索式：作者名="James R.Kermode"
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| 1 | Equivariant analytical mapping of first principles Hamiltonians to accurate and transferable materials models显示文摘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 represents the ab initio model to full order,rather than in two-centre or three-centre approximations.We achieve this by introducing an extension to the atomic cluster expansion(ACE)descriptor that represents Hamiltonian matrix blocks that transform equivariantly with respect to the full rotation group.The approach produces analytical linear models for the Hamiltonian and overlap matrices.Through an application to aluminium,we demonstrate that it is possible to train models from a handful of structures computed with density functional theory,and apply them to produce accurate predictions for the electronic structure.The model generalises well and is able to predict defects accurately from only bulk training data. | Liwei Zhang Berk Onat Geneviève Dusson Adam McSloy G.Anand Reinhard J.Maurer Christoph Ortner James R.Kermode | 2022 | npj Computational Materials2022，，1： | 2 |
| 2 | Compressing local atomic neighbourhood descriptors显示文摘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),scales as(NS)^(ν)whereν+1 is the body-order and N is the number of radial basis functions used in the density expansion.We introduce two distinct approaches which can be used to overcome this scaling for the SOAP power spectrum.Firstly,we show that the power spectrum is amenable to lossless compression with respect to both S and N,so that the descriptor length can be reduced from O(N^(2)S^(2))to O(NS).Secondly,we introduce a generalised SOAP kernel,where compression is achieved through the use of the total,element agnostic density,in combination with radial projection.The ideas used in the generalised kernel are equally applicably to any other body-ordered descriptors and we demonstrate this for the(ACSF). | James P.Darby James R.Kermode Gábor Csányi | 2022 | npj Computational Materials2022，，1： | 0 |
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