首页|Sichuan University Reports Findings in Machine Learning (De Novo Atomistic Discovery of Disordered Mechanical Metamaterials by Machine Learning)
Sichuan University Reports Findings in Machine Learning (De Novo Atomistic Discovery of Disordered Mechanical Metamaterials by Machine Learning)
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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."
ChengduPeople’s Republic of ChinaAsiaCyborgsEmerging TechnologiesMachine Learning
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