Is the Future of Materials Amorphous? Challenges and Opportunities in Simulations of Amorphous Materials
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866917839881895936 |
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| author | Madanchi, Ata Azek, Emna Zongo, Karim Béland, Laurent K. Mousseau, Normand Simine, Lena |
| author_facet | Madanchi, Ata Azek, Emna Zongo, Karim Béland, Laurent K. Mousseau, Normand Simine, Lena |
| contents | Amorphous solids form an enormous and underutilized class of materials. In order to drive the discovery of new useful amorphous materials further we need to achieve a closer convergence between computational and experimental methods. In this review, we highlight some of the important gaps between computational simulations and experiments, discuss popular state-of-the-art computational techniques such as the Activation Relaxation Technique nouveau (ARTn) and Reverse Monte Carlo (RMC), and introduce more recent advances: machine learning interatomic potentials (MLIPs) and generative machine learning for simulations of amorphous matter, e.g., the Morphological Autoregressive Protocol (MAP). Examples are drawn from the amorphous silicon and silica literature as well as from molecular glasses. Our outlook stresses the need for new computational methods to extend the time- and length- scales accessible through numerical simulations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_05035 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Is the Future of Materials Amorphous? Challenges and Opportunities in Simulations of Amorphous Materials Madanchi, Ata Azek, Emna Zongo, Karim Béland, Laurent K. Mousseau, Normand Simine, Lena Disordered Systems and Neural Networks Amorphous solids form an enormous and underutilized class of materials. In order to drive the discovery of new useful amorphous materials further we need to achieve a closer convergence between computational and experimental methods. In this review, we highlight some of the important gaps between computational simulations and experiments, discuss popular state-of-the-art computational techniques such as the Activation Relaxation Technique nouveau (ARTn) and Reverse Monte Carlo (RMC), and introduce more recent advances: machine learning interatomic potentials (MLIPs) and generative machine learning for simulations of amorphous matter, e.g., the Morphological Autoregressive Protocol (MAP). Examples are drawn from the amorphous silicon and silica literature as well as from molecular glasses. Our outlook stresses the need for new computational methods to extend the time- and length- scales accessible through numerical simulations. |
| title | Is the Future of Materials Amorphous? Challenges and Opportunities in Simulations of Amorphous Materials |
| topic | Disordered Systems and Neural Networks |
| url | https://arxiv.org/abs/2410.05035 |