Zobrazeno 1 - 10
of 403
pro vyhledávání: '"Ortner, Christoph"'
We investigate the nearsightedness property in the linear tight binding model at zero Fermi-temperature. We focus on the decay property of the density matrix for materials with indirect band gaps. By representing the density matrix in reciprocal spac
Externí odkaz:
http://arxiv.org/abs/2501.01188
We describe efficient differentiation methods for computing Jacobians and gradients of a large class of matrix functions including the matrix logarithm $\log(A)$ and $p$-th roots $A^{\frac{1}{p}}$. We exploit contour integrals and conformal maps as d
Externí odkaz:
http://arxiv.org/abs/2412.12598
Autor:
Rottler, Joerg, Ortner, Christoph
We explore the structural signatures of excitations in amorphous materials with the atomic cluster expansion (ACE), a universal and complete linear basis of descriptors of the atomic environment. Body-orderd linear classifiers are constructed that di
Externí odkaz:
http://arxiv.org/abs/2410.03216
Dynamics of coarse-grained particle systems derived via the Mori-Zwanzig projection formalism commonly take the form of a (generalized) Langevin equation with configuration-dependent friction and diffusion tensors. In this article, we introduce a cla
Externí odkaz:
http://arxiv.org/abs/2407.13935
We introduce and analyze numerical companion matrix methods for the reconstruction of hypersurfaces with crossings from smooth interpolants given unordered or, without loss of generality, value-sorted data. The problem is motivated by the desire to m
Externí odkaz:
http://arxiv.org/abs/2407.03731
A ubiquitous approach to obtain transferable machine learning-based models of potential energy surfaces for atomistic systems is to decompose the total energy into a sum of local atom-centred contributions. However, in many systems non-negligible lon
Externí odkaz:
http://arxiv.org/abs/2406.10915
The temperature-dependent behavior of defect densities within a crystalline structure is intricately linked to the phenomenon of vibrational entropy. Traditional methods for evaluating vibrational entropy are computationally intensive, limiting their
Externí odkaz:
http://arxiv.org/abs/2402.12744
The Atomic Cluster Expansion (ACE) (Drautz, Phys. Rev. B 99, 2019) has been widely applied in high energy physics, quantum mechanics and atomistic modeling to construct many-body interaction models respecting physical symmetries. Computational effici
Externí odkaz:
http://arxiv.org/abs/2401.01550
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
Machine-learned interatomic potentials (MLIPs) are typically trained on datasets that encompass a restricted subset of possible input structures, which presents a potential challenge for their generalization to a broader range of systems outside the
Externí odkaz:
http://arxiv.org/abs/2311.01664