Zobrazeno 1 - 10
of 227
pro vyhledávání: '"Hart, Gus"'
A central problem in data science is to use potentially noisy samples of an unknown function to predict function values for unseen inputs. In classical statistics, the predictive error is understood as a trade-off between the bias and the variance th
Externí odkaz:
http://arxiv.org/abs/2408.08294
Autor:
Owens, C. Braxton, Mathew, Nithin, Olaveson, Tyce W., Tavenner, Jacob P., Kober, Edward M., Tucker, Garritt J., Hart, Gus L. W., Homer, Eric R.
Obtaining microscopic structure-property relationships for grain boundaries are challenging because of the complex atomic structures that underlie their behavior. This has led to recent efforts to obtain these relationships with machine learning, but
Externí odkaz:
http://arxiv.org/abs/2407.21228
Solute segregation in materials with grain boundaries (GBs) has emerged as a popular method to thermodynamically stabilize nanocrystalline structures. However, the impact of varied GB crystallographic character on solute segregation has never been th
Externí odkaz:
http://arxiv.org/abs/2405.10566
We use the frozen phonon method to calculate the anharmonic potential energy surface and to model the ultrafast ferroelectric polarization reversal in LiNbO3 driven by intense pulses of THz light. Before stable switching of the polarization occurs, t
Externí odkaz:
http://arxiv.org/abs/2312.04732
Publikováno v:
Acta Materialia, Volume 274, 1 August 2024, 119962
Many material properties can be traced back to properties of their grain boundaries. Grain boundary energy (GBE), as a result, is a key quantity of interest in the analysis and modeling of microstructure. A standard method for calculating grain bound
Externí odkaz:
http://arxiv.org/abs/2312.00952
Technologies that function at room temperature often require magnets with a high Curie temperature, $T_\mathrm{C}$, and can be improved with better materials. Discovering magnetic materials with a substantial $T_\mathrm{C}$ is challenging because of
Externí odkaz:
http://arxiv.org/abs/2307.06879
Autor:
Luo, Yu, Meziere, Jason A., Samolyuk, German D., Hart, Gus L. W., Daymond, Mark R, Béland, Laurent Karim
Machine learning force fields (MLFFs) are an increasingly popular choice for atomistic simulations due to their high fidelity and improvable nature. Here, we propose a hybrid small-cell approach that combines attributes of both offline and active lea
Externí odkaz:
http://arxiv.org/abs/2306.00128
While machine-learned interatomic potentials have become a mainstay for modeling materials, designing training sets that lead to robust potentials is challenging. Automated methods, such as active learning and on-the-fly learning, construct reliable
Externí odkaz:
http://arxiv.org/abs/2304.01314
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
Autor:
Patrick, Matthew J., Rohrer, Gregory S., Chirayutthanasak, Ooraphan, Ratanaphan, Sutach, Homer, Eric R., Hart, Gus L. W., Epshteyn, Yekaterina, Barmak, Katayun
Publikováno v:
Acta Materialia, Volume 242, 1 January 2023, 118476
Grain boundary character distributions (GBCD) are routinely measured from bulk microcrystalline samples by electron backscatter diffraction (EBSD) and serial sectioning can be used to reconstruct relative grain boundary energy distributions (GBED) ba
Externí odkaz:
http://arxiv.org/abs/2207.02313