首页|Argonne National Laboratory Reports Findings in Machine Learning (Machine Learni ng-Based Investigation of Atomic Packing Effects: Chemical Pressures at the Extr emes of Intermetallic Complexity)
Argonne National Laboratory Reports Findings in Machine Learning (Machine Learni ng-Based Investigation of Atomic Packing Effects: Chemical Pressures at the Extr emes of Intermetallic Complexity)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Machine Learning is th e subject of a report. According to news reporting originating in Lemont, Illino is, by NewsRx journalists, research stated, “Intermetallic phases represent a do main of emergent behavior, in which atoms with packing and electronic preference s can combine into complex geometrical arrangements whose long-range order invol ves repeat patterns containing thousands of atoms or is incompatible with a 3D u nit cell. The formation of such arrangements points to unexplained driving force s within these systems that, if understood, could be harnessed in the design of new metallic materials.” The news reporters obtained a quote from the research from Argonne National Labo ratory, “DFTchemical pressure (CP) analysis has emerged as an approach to visua lize how atomic packing tensions within simpler crystal structures can drive thi s complexity and create potential functionality. However, the applications of th is method have hitherto been limited in scope by its dependence on resource-inte nsive electronic structure calculations. In this Article, we develop machine lea rning (ML)-based implementation of the CP approach, drawing on the collection of DFT-CP schemes in the Intermetallic Reactivity Database. We illustrate the meth od with comparisons of ML-CP and DFT-CP schemes for a series of examples, before demonstrating its application with an exploration of one of the quintessential instances of complexity in intermetallic chemistry, MgAl, whose high-temperature unit cell is a 2.8 nm cube containing 1227 atoms. An analysis of its ML-CP-deri ved interatomic pressures traces the origins of the structure to simple matching rules for the assembly of Frank-Kasper polyhedra.”
LemontIllinoisUnited StatesNorth a nd Central AmericaCyborgsEmerging TechnologiesMachine Learning
NETL
NSTL
