首页|National Center for Scientific Research (CNRS) Reports Findings in Machine Learning (Unravelling abnormal in-plane stretchability of two-dimensional metal-organic frameworks by machine learning potential molecular dynamics)
National Center for Scientific Research (CNRS) Reports Findings in Machine Learning (Unravelling abnormal in-plane stretchability of two-dimensional metal-organic frameworks by machine learning potential molecular dynamics)
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NETL
NSTL
Royal Soc Chemistry
New research on Machine Learning is the subject of a report. According to news reporting originating from Montpellier, France, by NewsRx correspondents, research stated, “Two-dimensional (2D) metal-organic frameworks (MOFs) hold immense potential for various applications due to their distinctive intrinsic properties compared to their 3D analogues. Herein, we designed a highly stable NiF(pyrazine) 2D MOF with a two-dimensional periodic wine-rack architecture.” Our news editors obtained a quote from the research from National Center for Scientific Research (CNRS), “Extensive first-principles calculations and molecular dynamics (MD) simulations based on a newly developed machine learning potential (MLP) revealed that this 2D MOF exhibits huge in-plane Poisson’s ratio anisotropy. This results in anomalous negative in-plane stretchability, as evidenced by an uncommon decrease in its in-plane area upon the application of uniaxial tensile strain, which makes this 2D MOF particularly attractive for flexible wearable electronics and ultra-thin sensor applications.”
NETL
NSTL
Royal Soc Chemistry
