Zobrazeno 1 - 10
of 462
pro vyhledávání: '"Kovacs, David A."'
Autor:
Csanády, Bálint, Nagy, Lóránt, Boros, Dániel, Ivkovic, Iván, Kovács, Dávid, Tóth-Lakits, Dalma, Márkus, László, Lukács, András
We present a purely deep neural network-based approach for estimating long memory parameters of time series models that incorporate the phenomenon of long-range dependence. Parameters, such as the Hurst exponent, are critical in characterizing the lo
Externí odkaz:
http://arxiv.org/abs/2410.03776
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
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
Autor:
Kovács, Dávid Péter, Moore, J. Harry, Browning, Nicholas J., Batatia, Ilyes, Horton, Joshua T., Kapil, Venkat, Witt, William C., Magdău, Ioan-Bogdan, Cole, Daniel J., Csányi, Gábor
Classical empirical force fields have dominated biomolecular simulation for over 50 years. Although widely used in drug discovery, crystal structure prediction, and biomolecular dynamics, they generally lack the accuracy and transferability required
Externí odkaz:
http://arxiv.org/abs/2312.15211
The MACE architecture represents the state of the art in the field of machine learning force fields for a variety of in-domain, extrapolation and low-data regime tasks. In this paper, we further evaluate MACE by fitting models for published benchmark
Externí odkaz:
http://arxiv.org/abs/2305.14247
Data-driven interatomic potentials have emerged as a powerful class of surrogate models for {\it ab initio} potential energy surfaces that are able to reliably predict macroscopic properties with experimental accuracy. In generating accurate and tran
Externí odkaz:
http://arxiv.org/abs/2210.04225
Autor:
Darby, James P., Kovács, Dávid P., Batatia, Ilyes, Caro, Miguel A., Hart, Gus L. W., Ortner, Christoph, Csányi, Gábor
Density based representations of atomic environments that are invariant under Euclidean symmetries have become a widely used tool in the machine learning of interatomic potentials, broader data-driven atomistic modelling and the visualisation and ana
Externí odkaz:
http://arxiv.org/abs/2210.01705
Creating fast and accurate force fields is a long-standing challenge in computational chemistry and materials science. Recently, several equivariant message passing neural networks (MPNNs) have been shown to outperform models built using other approa
Externí odkaz:
http://arxiv.org/abs/2206.07697
We generalize subgraph densities, arising in dense graph limit theory, to Markov spaces (symmetric measures on the square of a standard Borel space). More generally, we define an analogue of the set of homomorphisms in the form of a measure on maps o
Externí odkaz:
http://arxiv.org/abs/2206.04493
Autor:
Géhberger, Dániel, Kovács, Dávid
Serverless execution and most notably the Function as a Service (FaaS) model got quite some attention during the recent years. As of today, all commercial and open source implementations follow the common practice of keeping the execution environment
Externí odkaz:
http://arxiv.org/abs/2206.00599