首页|Swiss Federal Institute of Technology Lausanne (EPFL) Reports Findings in Machin e Learning (Expanding density-correlation machine learning representations for a nisotropic coarse-grained particles)
Swiss Federal Institute of Technology Lausanne (EPFL) Reports Findings in Machin e Learning (Expanding density-correlation machine learning representations for a nisotropic coarse-grained particles)
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 DailyNews Daily News – New research on Machine Learning is th e subject of a report. According to newsreporting originating from Lausanne, Sw itzerland, by NewsRx correspondents, research stated, “Physicsbased,atom-cente red machine learning (ML) representations have been instrumental to the effectiv eintegration of ML within the atomistic simulation community. Many of these rep resentations build off theidea of atoms as having spherical, or isotropic, inte ractions.”
NETL
NSTL
