Recent Advances in Metallic Glasses
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arXiv
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| Auteurs principaux: | , , , , , , , , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866918254817050624 |
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| author | Bonfanti, Silvia Busch, Ralf Byggmästar, Jesper Dyre, Jeppe C. Eckert, Jürgen Fajardo, Spencer Falk, Michael L. Gallino, Isabella Kruzic, Jamie J. Lu, Jiayin Monaco, Giulio Ozawa, Misaki Parmar, Anshul D. S. Rycroft, Chris H. Sastry, Srikanth |
| author_facet | Bonfanti, Silvia Busch, Ralf Byggmästar, Jesper Dyre, Jeppe C. Eckert, Jürgen Fajardo, Spencer Falk, Michael L. Gallino, Isabella Kruzic, Jamie J. Lu, Jiayin Monaco, Giulio Ozawa, Misaki Parmar, Anshul D. S. Rycroft, Chris H. Sastry, Srikanth |
| contents | This paper reviews recent advances in the field of metallic glasses, focusing on the development of novel experimental techniques and in silico models. We discuss progress in experimental characterization, additive manufacturing, multiscale modeling approaches, and the growing role of machine learning in understanding and designing these complex materials. On the experimental side, we highlight measurements of thermophysical properties of supercooled liquids via fast chip calorimetry and enhancements in mechanical properties through rejuvenation treatments. This work underscores the crucial role of short-range order and medium-range order in controlling metallic glass mechanical properties. Recent progress in structural probes allows in situ observations of deformation mechanisms, positioning the field well to further advance our understanding of mechanical properties. Additive manufacturing of metallic glasses is discussed as one encouraging new manufacturing route for metallic glasses. We examine laser powder-bed fusion process physics and the central trade-off between amorphicity and densification, including heat affected zone devitrification and defects formation, together with emerging mitigation strategies and applications. On the theoretical and simulation side, we review advances in nanoscale, mesoscale, and continuum modeling of metallic glasses that have led to promising approaches by which multiscale schemes can incorporate data sourced from atomic-scale simulations. These efforts have helped to elucidate the connection between the glass structure and mechanical and rheological responses. We also cover the development of machine learning interatomic potentials for metallic glasses, along with machine learning driven prediction of glass forming ability and inverse design methods. Finally, challenges and directions for future research are presented and discussed. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_16590 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Recent Advances in Metallic Glasses Bonfanti, Silvia Busch, Ralf Byggmästar, Jesper Dyre, Jeppe C. Eckert, Jürgen Fajardo, Spencer Falk, Michael L. Gallino, Isabella Kruzic, Jamie J. Lu, Jiayin Monaco, Giulio Ozawa, Misaki Parmar, Anshul D. S. Rycroft, Chris H. Sastry, Srikanth Materials Science Disordered Systems and Neural Networks This paper reviews recent advances in the field of metallic glasses, focusing on the development of novel experimental techniques and in silico models. We discuss progress in experimental characterization, additive manufacturing, multiscale modeling approaches, and the growing role of machine learning in understanding and designing these complex materials. On the experimental side, we highlight measurements of thermophysical properties of supercooled liquids via fast chip calorimetry and enhancements in mechanical properties through rejuvenation treatments. This work underscores the crucial role of short-range order and medium-range order in controlling metallic glass mechanical properties. Recent progress in structural probes allows in situ observations of deformation mechanisms, positioning the field well to further advance our understanding of mechanical properties. Additive manufacturing of metallic glasses is discussed as one encouraging new manufacturing route for metallic glasses. We examine laser powder-bed fusion process physics and the central trade-off between amorphicity and densification, including heat affected zone devitrification and defects formation, together with emerging mitigation strategies and applications. On the theoretical and simulation side, we review advances in nanoscale, mesoscale, and continuum modeling of metallic glasses that have led to promising approaches by which multiscale schemes can incorporate data sourced from atomic-scale simulations. These efforts have helped to elucidate the connection between the glass structure and mechanical and rheological responses. We also cover the development of machine learning interatomic potentials for metallic glasses, along with machine learning driven prediction of glass forming ability and inverse design methods. Finally, challenges and directions for future research are presented and discussed. |
| title | Recent Advances in Metallic Glasses |
| topic | Materials Science Disordered Systems and Neural Networks |
| url | https://arxiv.org/abs/2512.16590 |