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1篇 您的检索式：作者名="April M.Cooper"

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		题名	作者	年代	出处	被引量
	1	Efficient training of ANN potentials by including atomic forces via Taylor expansion and application to water and a transition-metal oxide显示文摘Artificial neural network(ANN)potentials enable the efficient large-scale atomistic modeling of complex materials with near firstprinciples accuracy.For molecular dynamics simulations,accurate energies and interatomic forces are a prerequisite,but training ANN potentials simultaneously on energies and forces from electronic structure calculations is computationally demanding.Here,we introduce an efficient alternative method for the training of ANN potentials on energy and force information,based on an extrapolation of the total energy via a Taylor expansion.By translating the force information to approximate energies,the quadratic scaling with the number of atoms exhibited by conventional force-training methods can be avoided,which enables the training on reference datasets containing complex atomic structures.	April M.Cooper Johannes Kästner Alexander Urban Nongnuch Artrith	2020	npj Computational Materials2020，，1：	3

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