Výsledky vyhledávání - "Margraf, Johannes"

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Efficient Composite Infrared Spectroscopy: Combining the Doubly-Harmonic Approximation with Machine Learning Potentials

Autor: Pracht, Philipp, Pillai, Yuthika, Kapil, Venkat, Csányi, Gábor, Gönnheimer, Nils, Vondrák, Martin, Margraf, Johannes T., Wales, David J.

Vibrational spectroscopy is a cornerstone technique for molecular characterization and offers an ideal target for the computational investigation of molecular materials. Building on previous comprehensive assessments of efficient methods for infrared

Externí odkaz: http://arxiv.org/abs/2408.08174

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A foundation model for atomistic materials chemistry

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

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Operando Characterization and Molecular Simulations Reveal the Growth Kinetics of Graphene on Liquid Copper during Chemical Vapor Deposition

Autor: Rein, Valentina, Gao, Hao, Heenen, Hendrik H., Sghaier, Wissal, Manikas, Anastasios C., Saedi, Mehdi, Margraf, Johannes T., Galiotis, Costas, Renaud, Gilles, Konovalov, Oleg V., Groot, Irene M. N., Reuter, Karsten, Jankowski, Maciej

Publikováno v: ACS Nano 2024

In recent years, liquid metal catalysts have emerged as a compelling choice for the controllable, large-scale, and high-quality synthesis of two-dimensional materials. At present, there is little mechanistic understanding of the intricate catalytic p

Externí odkaz: http://arxiv.org/abs/2305.18331

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Akademický článek

Small basis set density functional theory method for cost-efficient, large-scale condensed matter simulations.

Autor: Keller, Elisabeth, Morgenstein, Jack, Reuter, Karsten, Margraf, Johannes T.

Publikováno v: Journal of Chemical Physics; 8/21/2024, Vol. 161 Issue 7, p1-10, 10p

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Finding the Right Bricks for Molecular Lego: A Data Mining Approach to Organic Semiconductor Design

Autor: Kunkel, Christian, Schober, Christoph, Margraf, Johannes T., Reuter, Karsten, Oberhofer, Harald

Publikováno v: Chem. Mater. 31, 969 (2019)

Improving charge carrier mobilities in organic semiconductors is a challenging task that has hitherto primarily been tackled by empirical structural tuning of promising core compounds. Knowledge-based methods can greatly accelerate such local explora

Externí odkaz: http://arxiv.org/abs/2110.09149

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Fast and Uncertainty-Aware Directional Message Passing for Non-Equilibrium Molecules

Autor: Gasteiger, Johannes, Giri, Shankari, Margraf, Johannes T., Günnemann, Stephan

Many important tasks in chemistry revolve around molecules during reactions. This requires predictions far from the equilibrium, while most recent work in machine learning for molecules has been focused on equilibrium or near-equilibrium states. In t

Externí odkaz: http://arxiv.org/abs/2011.14115

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Atomic structures and orbital energies of 61,489 crystal-forming organic molecules

Autor: Stuke, Annika, Kunkel, Christian, Golze, Dorothea, Todorović, Milica, Margraf, Johannes T., Reuter, Karsten, Rinke, Patrick, Oberhofer, Harald

Data science and machine learning in materials science require large datasets of technologically relevant molecules or materials. Currently, publicly available molecular datasets with realistic molecular geometries and spectral properties are rare. W

Externí odkaz: http://arxiv.org/abs/2001.08954

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