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.
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.
