On the interplay of liquid-like and stress-driven dynamics in a metallic glass former observed by temperature scanning XPCS
Authors:
Maximilian Frey,
Nico Neuber,
Sascha Sebastian Riegler,
Antoine Cornet,
Yuriy Chushkin,
Federico Zontone,
Lucas Ruschel,
Bastian Adam,
Mehran Nabahat,
Fan Yang,
Jie Shen,
Fabian Westermeier,
Michael Sprung,
Daniele Cangialosi,
Valerio Di Lisio,
Isabella Gallino,
Ralf Busch,
Beatrice Ruta,
Eloi Pineda
Abstract:
Modern detector technology and highly brilliant fourth-generation synchrotrons allow to improve the temporal resolution in time-resolved diffraction studies. Profiting from this, we applied temperature scanning X-ray photon correlation spectroscopy (XPCS) to probe the dynamics of a Pt-based metallic glass former in the glass, glass transition region, and supercooled liquid, covering up to six orde…
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Modern detector technology and highly brilliant fourth-generation synchrotrons allow to improve the temporal resolution in time-resolved diffraction studies. Profiting from this, we applied temperature scanning X-ray photon correlation spectroscopy (XPCS) to probe the dynamics of a Pt-based metallic glass former in the glass, glass transition region, and supercooled liquid, covering up to six orders of magnitude in time scales. Our data demonstrates that the structural alpha-relaxation process is still observable in the glass, although it is partially masked by a faster source of decorrelation observed at atomic scale. We present an approach that interprets these findings as the superposition of heterogeneous liquid-like and stress-driven ballistic-like atomic motions. This work not only extends the dynamical range probed by standard isothermal XPCS, but also clarifies the fate of the alpha-relaxation across the glass transition and provides a new perception on the anomalous, compressed temporal decay of the density-density correlation functions observed in metallic glasses and many out-of-equilibrium soft materials.
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Submitted 22 March, 2024; v1 submitted 18 March, 2024;
originally announced March 2024.
Physics-Based Machine Learning Approach for Modeling the Temperature-Dependent Yield Strength of Superalloys
Authors:
Baldur Steingrimsson,
Xuesong Fan,
Benjamin Adam,
Peter K. Liaw
Abstract:
In the pursuit of developing high-temperature alloys with improved properties for meeting the performance requirements of next-generation energy and aerospace demands, integrated computational materials engineering (ICME) has played a crucial role. In this paper a machine learning (ML) approach is presented, capable of predicting the temperature-dependent yield strengths of superalloys, utilizing…
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In the pursuit of developing high-temperature alloys with improved properties for meeting the performance requirements of next-generation energy and aerospace demands, integrated computational materials engineering (ICME) has played a crucial role. In this paper a machine learning (ML) approach is presented, capable of predicting the temperature-dependent yield strengths of superalloys, utilizing a bilinear log model. Importantly, the model introduces the parameter break temperature, $T_{break}$, which serves as an upper boundary for operating conditions, ensuring acceptable mechanical performance. In contrast to conventional black-box approaches, our model is based on the underlying fundamental physics, directly built into the model. We present a technique of global optimization, one allowing the concurrent optimization of model parameters over the low-temperature and high-temperature regimes. The results presented extend previous work on high-entropy alloys (HEAs) and offer further support for the bilinear log model and its applicability for modeling the temperature-dependent strength behavior of superalloys as well as HEAs.
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Submitted 29 November, 2022;
originally announced November 2022.