தேடலை உயர் செயல்திறன் கணினி கொத்து News Today : Breaking News, Live Updates & Top Stories | Vimarsana

 E-Mail
Northwestern University researchers have developed a new framework using machine learning that improves the accuracy of interatomic potentials the guiding rules describing how atoms interact in new materials design. The findings could lead to more accurate predictions of how new materials transfer heat, deform, and fail at the atomic scale.
Designing new nanomaterials is an important aspect of developing next-generation devices used in electronics, sensors, energy harvesting and storage, optical detectors, and structural materials. To design these materials, researchers create interatomic potentials through atomistic modeling, a computational approach that predicts how these materials behave by accounting for their properties at the smallest level. The process to establish materials interatomic potential called parameterization has required significant chemical and physical intuition, leading to less accurate prediction of new materials design. ....

Jose Mendoza , Xu Zhang , Nancyj Farley , Horacio Espinosa , Jeffreyt Paci , Hoang Nguyen , Subramanian Sankaranarayanan , University Of Victoria , Michigan State University , Us Department Of Energy Office Science , National Science Foundation , Nanoscale Materials At Argonne National Laboratory , Office Of Basic Energy Sciences , Office Of Science , Argonne National Laboratory , Northwestern University , Interatomic Potentials , Statistical Analyses , Case Study , Nature Partner Journals , Northwestern Engineering , Applied Mechanics , Michigan State , Nanoscale Materials , Quest High Performance Computing Cluster , Basic Energy Sciences ,