Zobrazeno 1 - 10
of 1 279
pro vyhledávání: '"Scheffler Matthias"'
Autor:
Moerman, Evgeny, Gallo, Alejandro, Irmler, Andreas, Schäfer, Tobias, Hummel, Felix, Grüneis, Andreas, Scheffler, Matthias
We investigate the convergence of quasi-particle energies for periodic systems to the thermodynamic limit using increasingly large simulation cells corresponding to increasingly dense integration meshes in reciprocal space. The quasi-particle energie
Externí odkaz:
http://arxiv.org/abs/2409.03721
First-principle approaches for phonon-limited electronic transport are typically based on many-body perturbation theory and transport equations. With that, they rely on the validity of the quasi-particle picture for electrons and phonons, which is kn
Externí odkaz:
http://arxiv.org/abs/2408.12908
Autor:
Foppa, Lucas, Scheffler, Matthias
Using a modest amount of data from a large population, subgroup discovery (SGD) identifies outstanding subsets of data with respect to a certain property of interest of that population. The SGs are described by "rules". These are constraints on key d
Externí odkaz:
http://arxiv.org/abs/2403.18437
Autor:
Kokott, Sebastian, Merz, Florian, Yao, Yi, Carbogno, Christian, Rossi, Mariana, Havu, Ville, Rampp, Markus, Scheffler, Matthias, Blum, Volker
Publikováno v:
J. Chem. Phys. 161, 024112 (2024)
Hybrid density functional approximations (DFAs) offer compelling accuracy for ab initio electronic-structure simulations of molecules, nanosystems, and bulk materials, addressing some deficiencies of computationally cheaper, frequently used semilocal
Externí odkaz:
http://arxiv.org/abs/2403.10343
Autor:
Bauer, Stefan, Benner, Peter, Bereau, Tristan, Blum, Volker, Boley, Mario, Carbogno, Christian, Catlow, C. Richard A., Dehm, Gerhard, Eibl, Sebastian, Ernstorfer, Ralph, Fekete, Ádám, Foppa, Lucas, Fratzl, Peter, Freysoldt, Christoph, Gault, Baptiste, Ghiringhelli, Luca M., Giri, Sajal K., Gladyshev, Anton, Goyal, Pawan, Hattrick-Simpers, Jason, Kabalan, Lara, Karpov, Petr, Khorrami, Mohammad S., Koch, Christoph, Kokott, Sebastian, Kosch, Thomas, Kowalec, Igor, Kremer, Kurt, Leitherer, Andreas, Li, Yue, Liebscher, Christian H., Logsdail, Andrew J., Lu, Zhongwei, Luong, Felix, Marek, Andreas, Merz, Florian, Mianroodi, Jaber R., Neugebauer, Jörg, Pei, Zongrui, Purcell, Thomas A. R., Raabe, Dierk, Rampp, Markus, Rossi, Mariana, Rost, Jan-Michael, Saal, James, Saalmann, Ulf, Sasidhar, Kasturi Narasimha, Saxena, Alaukik, Sbailò, Luigi, Scheidgen, Markus, Schloz, Marcel, Schmidt, Daniel F., Teshuva, Simon, Trunschke, Annette, Wei, Ye, Weikum, Gerhard, Xian, R. Patrick, Yao, Yi, Yin, Junqi, Zhao, Meng, Scheffler, Matthias
Science is and always has been based on data, but the terms "data-centric" and the "4th paradigm of" materials research indicate a radical change in how information is retrieved, handled and research is performed. It signifies a transformative shift
Externí odkaz:
http://arxiv.org/abs/2402.10932
Publikováno v:
J. Chem. Phys. 160, 034106 (2024)
Semilocal density-functional approximations (DFAs), including the state-of-the-art SCAN functional, are plagued by the self-interaction error (SIE). While this error is explicitly defined only for one-electron systems, it has inspired the self-intera
Externí odkaz:
http://arxiv.org/abs/2401.11696
Autor:
Boley, Mario, Luong, Felix, Teshuva, Simon, Schmidt, Daniel F, Foppa, Lucas, Scheffler, Matthias
Materials discovery driven by statistical property models is an iterative decision process, during which an initial data collection is extended with new data proposed by a model-informed acquisition function--with the goal to maximize a certain "rewa
Externí odkaz:
http://arxiv.org/abs/2311.15549
Autor:
Foppa, Lucas, Scheffler, Matthias
Artificial intelligence (AI) can accelerate the design of materials by identifying correlations and complex patterns in data. However, AI methods commonly attempt to describe the entire, immense materials space with a single model, while it is typica
Externí odkaz:
http://arxiv.org/abs/2311.10381
Machine-learning (ML) interatomic potentials (IPs) trained on first-principles datasets are becoming increasingly popular since they promise to treat larger system sizes and longer time scales, compared to the {\em ab initio} techniques producing the
Externí odkaz:
http://arxiv.org/abs/2309.00195
Accurate and explainable artificial-intelligence (AI) models are promising tools for the acceleration of the discovery of new materials, ore new applications for existing materials. Recently, symbolic regression has become an increasingly popular too
Externí odkaz:
http://arxiv.org/abs/2305.01242