Zobrazeno 1 - 10
of 236
pro vyhledávání: '"Papanikolaou, Stefanos"'
Autor:
Angeletti, Andrea, Leoni, Luca, Massa, Dario, Pasquini, Luca, Papanikolaou, Stefanos, Franchini, Cesare
Understanding and accurately predicting hydrogen diffusion in materials is challenging due to the complex interactions between hydrogen defects and the crystal lattice. These interactions span large length and time scales, making them difficult to ad
Externí odkaz:
http://arxiv.org/abs/2407.21088
Autor:
Kaszuba, Grzegorz, Naghdi, Amirhossein D., Massa, Dario, Papanikolaou, Stefanos, Jaszkiewicz, Andrzej, Sankowski, Piotr
Large language models manifest the ability of few-shot adaptation to a sequence of provided examples. This behavior, known as in-context learning, allows for performing nontrivial machine learning tasks during inference only. In this work, we address
Externí odkaz:
http://arxiv.org/abs/2406.01808
Autor:
Owen, Cameron J., Naghdi, Amirhossein D., Johansson, Anders, Massa, Dario, Papanikolaou, Stefanos, Kozinsky, Boris
Crystal dislocation dynamics, especially at high temperatures, represents a subject where experimental phenomenological input is commonly required, and parameter-free predictions, starting from quantum methods, have been beyond reach. This is especia
Externí odkaz:
http://arxiv.org/abs/2401.04359
Autor:
Kaszuba, Grzegorz, Dorabati, Amirhossein Naghdi, Papanikolaou, Stefanos, Jaszkiewicz, Andrzej, Sankowski, Piotr
Exploring the vast composition space of multi-component alloys presents a challenging task for both \textit{ab initio} (first principles) and experimental methods due to the time-consuming procedures involved. This ultimately impedes the discovery of
Externí odkaz:
http://arxiv.org/abs/2312.16073
Autor:
Naghdi, Amirhossein D., Pellegrini, Franco, Küçükbenli, Emine, Massa, Dario, Gutierrez, F. Javier Dominguez, Kaxiras, Efthimios, Papanikolaou, Stefanos
Material characterization in nano-mechanical tests requires precise interatomic potentials for the computation of atomic energies and forces with near-quantum accuracy. For such purposes, we develop a robust neural-network interatomic potential (NNIP
Externí odkaz:
http://arxiv.org/abs/2312.10856
Under plastic flow, multi-element high/medium-entropy alloys (HEAs/MEAs) commonly exhibit complex intermittent and collective dislocation dynamics owing to inherent lattice distortion and atomic-level chemical complexities. Using atomistic simulation
Externí odkaz:
http://arxiv.org/abs/2310.03828
Materials design has traditionally evolved through trial-error approaches, mainly due to the non-local relationship between microstructures and properties such as strength and toughness. We propose 'alloy informatics' as a machine learning based prot
Externí odkaz:
http://arxiv.org/abs/2309.13673
Autor:
Karimi, Kamran, Papanikolaou, Stefanos
Shear localization in slowly-driven bulk metallic glasses (BMGs) is typically accompanied by a sharp drop in the bulk stress response as a signature of the plastic yielding transition. It is also observed that the sharpness of this elastic-plastic dy
Externí odkaz:
http://arxiv.org/abs/2309.11867
Materials discovery, especially for applications that require extreme operating conditions, requires extensive testing that naturally limits the ability to inquire the wealth of possible compositions. Machine Learning (ML) has nowadays a well establi
Externí odkaz:
http://arxiv.org/abs/2306.10766
Autor:
Karimi, Kamran, Papanikolaou, Stefanos
Complex, concentrated, multi-component alloys have been shown to display outstanding thermo-mechanical properties, that have been typically attributed to sluggish diffusion, entropic, and lattice distortion effects. Here, we investigate two metal all
Externí odkaz:
http://arxiv.org/abs/2304.04255