Machine-learned interatomic potentials for transition metal dichalcogenide Mo_(1−x)W_(x)S_(2−2y)Se_(2y) alloys  

作　　者：Anas Siddiqui Nicholas D.M.Hine 

机构地区：[1]Department of Physics,University of Warwick,Coventry,CV47AL,UK

出　　处：《npj Computational Materials》2024年第1期1497-1507,共11页计算材料学(英文)

基　　金：funding from the EPSRC CDT in Modelling of Heterogeneous Systems funded by EP/S022848/1;N.D.M.H.acknowledges support from EPSRC grant number EP/V000136/1;Computing facilities were provided by the Scientific Computing Research Technology Platform of the University of Warwick through the use of the High Performance Computing(HPC)cluster Avon,and the Sulis Tier 2 platforms at HPC Midlands+funded by the Engineering and Physical Sciences Research Council(EPSRC),grant number EP/T022108/1.

摘　　要：Machine Learned Interatomic Potentials(MLIPs)combine the predictive power of Density Functional Theory(DFT)with the speed and scaling of interatomic potentials,enabling theoretical spectroscopy to be applied to larger and more complex systems than is possible with DFT.

关 键 词：SCALING interatomic COMBINE 

分 类 号：O73[理学—晶体学]