Abstract
New research on Machine Learning is the subject of a report. According to news originating from Chengdu, People's Republic of China, by NewsRx correspondents, research stated, “Archi- tected materials design across orders of magnitude length scale intrigues exceptional mechanical responses nonexistent in their natural bulk state. However, the so-termed mechanical metamaterials, when scaling bottom down to the atomistic or microparticle level, remain largely unexplored and conventionally fall out of their coarse-resolution, ordered-pattern design space.” Our news journalists obtained a quote from the research from Sichuan University, “Here, combining high-throughput molecular dynamics (MD) simulations and machine learning (ML) strategies, some in- triguing atomistic families of disordered mechanical metamaterials are discovered, as fabricated by melt quenching and exemplified herein by lightweight-yet-stiff cellular materials featuring a theoretical limit of linear stiffness-density scaling, whose structural disorder-rather than order-is key to reduce the scaling ex- ponent and is simply controlled by the bonding interactions and their directionality that enable flexible tunability experimentally. Importantly, a systematic navigation in the forcefield landscape reveals that, in-between directional and non-directional bonding such as covalent and ionic bonds, modest bond di- rectionality is most likely to promotes disordered packing of polyhedral, stretching-dominated structures responsible for the formation of metamaterials."
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