Zobrazeno 1 - 10
of 335
pro vyhledávání: '"Csányi, Gabor"'
Recent advances in machine learning have led to foundation models for atomistic materials chemistry, potentially enabling quantum-accurate descriptions of interatomic forces at reduced computational cost. These models are benchmarked by predicting ma
Externí odkaz:
http://arxiv.org/abs/2408.00755
A survey of the contributions to the Journal of Chemical Physics' Special Topic on Software for Atomistic Machine Learning.
Externí odkaz:
http://arxiv.org/abs/2406.19750
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
Autor:
Kaur, Harveen, Della Pia, Flaviano, Batatia, Ilyes, Advincula, Xavier R., Shi, Benjamin X., Lan, Jinggang, Csányi, Gábor, Michaelides, Angelos, Kapil, Venkat
Calculating sublimation enthalpies of molecular crystal polymorphs is relevant to a wide range of technological applications. However, predicting these quantities at first-principles accuracy -- even with the aid of machine learning potentials -- is
Externí odkaz:
http://arxiv.org/abs/2405.20217
Free energies play a central role in characterising the behaviour of chemical systems and are among the most important quantities that can be calculated by molecular dynamics simulations. The free energy of hydration in particular is a well-studied p
Externí odkaz:
http://arxiv.org/abs/2405.18171
Autor:
Stark, Wojciech G., van der Oord, Cas, Batatia, Ilyes, Zhang, Yaolong, Jiang, Bin, Csányi, Gábor, Maurer, Reinhard J.
Simulations of chemical reaction probabilities in gas surface dynamics require the calculation of ensemble averages over many tens of thousands of reaction events to predict dynamical observables that can be compared to experiments. At the same time,
Externí odkaz:
http://arxiv.org/abs/2403.15334
Autor:
Karimitari, Nima, Baldwin, William J., Muller, Evan W., Bare, Zachary J. L., Kennedy, W. Joshua, Csányi, Gábor, Sutton, Christopher
Low dimensional hybrid organic-inorganic perovskites (HOIPs) represent a promising class of electronically active materials for both light absorption and emission. The design space of HOIPs is extremely large, since a diverse space of organic cations
Externí odkaz:
http://arxiv.org/abs/2403.06955
Autor:
Elijošius, Rokas, Zills, Fabian, Batatia, Ilyes, Norwood, Sam Walton, Kovács, Dávid Péter, Holm, Christian, Csányi, Gábor
Generative modelling aims to accelerate the discovery of novel chemicals by directly proposing structures with desirable properties. Recently, score-based, or diffusion, generative models have significantly outperformed previous approaches. Key to th
Externí odkaz:
http://arxiv.org/abs/2402.08708
Lattices are architected metamaterials whose properties strongly depend on their geometrical design. The analogy between lattices and graphs enables the use of graph neural networks (GNNs) as a faster surrogate model compared to traditional methods s
Externí odkaz:
http://arxiv.org/abs/2401.16914
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