Zobrazeno 1 - 10
of 219
pro vyhledávání: '"Bernstein, Noam"'
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:
Witt, William C., van der Oord, Cas, Gelžinytė, Elena, Järvinen, Teemu, Ross, Andres, Darby, James P., Ho, Cheuk Hin, Baldwin, William J., Sachs, Matthias, Kermode, James, Bernstein, Noam, Csányi, Gábor, Ortner, Christoph
We introduce ACEpotentials.jl, a Julia-language software package that constructs interatomic potentials from quantum mechanical reference data using the Atomic Cluster Expansion (Drautz, 2019). As the latter provides a complete description of atomic
Externí odkaz:
http://arxiv.org/abs/2309.03161
Autor:
Gelžinytė, Elena, Wengert, Simon, Stenczel, Tamás K., Heenen, Hendrik H., Reuter, Karsten, Csányi, Gábor, Bernstein, Noam
Predictive atomistic simulations are increasingly employed for data intensive high throughput studies that take advantage of constantly growing computational resources. To handle the sheer number of individual calculations that are needed in such stu
Externí odkaz:
http://arxiv.org/abs/2306.11421
Autor:
Ashton, Greg, Bernstein, Noam, Buchner, Johannes, Chen, Xi, Csányi, Gábor, Fowlie, Andrew, Feroz, Farhan, Griffiths, Matthew, Handley, Will, Habeck, Michael, Higson, Edward, Hobson, Michael, Lasenby, Anthony, Parkinson, David, Pártay, Livia B., Pitkin, Matthew, Schneider, Doris, Speagle, Joshua S., South, Leah, Veitch, John, Wacker, Philipp, Wales, David J., Yallup, David
Publikováno v:
Nature Reviews Methods Primers volume 2, Article number: 39 (2022)
We review Skilling's nested sampling (NS) algorithm for Bayesian inference and more broadly multi-dimensional integration. After recapitulating the principles of NS, we survey developments in implementing efficient NS algorithms in practice in high-d
Externí odkaz:
http://arxiv.org/abs/2205.15570
Autor:
Deringer, Volker L., Bernstein, Noam, Csányi, Gábor, Wilson, Mark, Drabold, David A., Elliott, Stephen R.
Structurally disordered materials continue to pose fundamental questions, including that of how different disordered phases ("polyamorphs") can coexist and transform from one to another. As a widely studied case, amorphous silicon (a-Si) forms a four
Externí odkaz:
http://arxiv.org/abs/1912.07344
Publikováno v:
Phys. Rev. B 100, 205204 (2019)
Using first-principles calculations we determine the role of compressive and tensile uniaxial and equibiaxial strain on the structural, electronic and magnetic properties of V$_2$O$_3$. We find that compressive strain increases the energy cost to tra
Externí odkaz:
http://arxiv.org/abs/1909.13422
Autor:
Rosenbrock, Conrad W., Gubaev, Konstantin, Shapeev, Alexander V., Pártay, Livia B., Bernstein, Noam, Csányi, Gábor, Hart, Gus L. W.
We introduce machine-learned potentials for Ag-Pd to describe the energy of alloy configurations over a wide range of compositions. We compare two different approaches. Moment tensor potentials (MTP) are polynomial-like functions of interatomic dista
Externí odkaz:
http://arxiv.org/abs/1906.07816
Interatomic potential models based on machine learning (ML) are rapidly developing as tools for materials simulations. However, because of their flexibility, they require large fitting databases that are normally created with substantial manual selec
Externí odkaz:
http://arxiv.org/abs/1905.10407
Publikováno v:
Phys. Rev. B 99, 214103 (2019)
The vanadates VO$_2$ and V$_2$O$_3$ are prototypical examples of strongly correlated materials that exhibit a metal-insulator transition. While the phase transitions in these materials have been studied extensively, there is a limited understanding o
Externí odkaz:
http://arxiv.org/abs/1902.04526
Autor:
Bernstein, Noam, Bhattarai, Bishal, Csányi, Gábor, Drabold, David A., Elliott, Stephen R., Deringer, Volker L.
Publikováno v:
Angew. Chem. Int. Ed. 58, 7057 (2019)
Amorphous materials are coming within reach of realistic computer simulations, but new approaches are needed to fully understand their intricate atomic structures. Here, we show how machine-learning (ML)-based techniques can give new, quantitative ch
Externí odkaz:
http://arxiv.org/abs/1811.11069