Large scale atomistic simulations provide direct access to important materials phenomena not easily accessible to experiments or quantum mechanics-based calculation approaches.Accurate and efficient interatomic potent...Large scale atomistic simulations provide direct access to important materials phenomena not easily accessible to experiments or quantum mechanics-based calculation approaches.Accurate and efficient interatomic potentials are the key enabler,but their development remains a challenge for complex materials and/or complex phenomena.Machine learning potentials,such as the Deep Potential(DP)approach,provide robust means to produce general purpose interatomic potentials.Here,we provide a methodology for specialising machine learning potentials for high fidelity simulations of complex phenomena,where general potentials do not suffice.As an example,we specialise a general purpose DP method to describe the mechanical response of two allotropes of titanium(in addition to other defect,thermodynamic and structural properties).The resulting DP correctly captures the structures,energies,elastic constants andγ-lines of Ti in both the HCP and BCC structures,as well as properties such as dislocation core structures,vacancy formation energies,phase transition temperatures,and thermal expansion.The DP thus enables direct atomistic modelling of plastic and fracture behaviour of Ti.The approach to specialising DP interatomic potential,DPspecX,for accurate reproduction of properties of interest“X”,is general and extensible to other systems and properties.展开更多
To fill the gap between accurate(and expensive)ab initio calculations and efficient atomistic simulations based on empirical interatomic potentials,a new class of descriptions of atomic interactions has emerged and be...To fill the gap between accurate(and expensive)ab initio calculations and efficient atomistic simulations based on empirical interatomic potentials,a new class of descriptions of atomic interactions has emerged and been widely applied;i.e.machine learning potentials(MLPs).One recently developed type of MLP is the deep potential(DP)method.In this review,we provide an introduction to DP methods in computational materials science.The theory underlying the DP method is presented along with a step-by-step introduction to their development and use.We also review materials applications of DPs in a wide range of materials systems.The DP Library provides a platform for the development of DPs and a database of extant DPs.We discuss the accuracy and efficiency of DPs compared with ab initio methods and empirical potentials.展开更多
基金This work is supported by the Research Grants Council,Hong Kong SAR through the Collaborative Research Fund under project number 8730054Early Career Scheme Fund under project number 21205019+1 种基金T.Q.W.acknowledges the support of the Hong Kong institute for Advanced Study,City University of Hong Kong through a postdoctoral fellowship.The work of H.W.is supported by the National Science Foundation of China under Grant No.11871110the Beijing Academy of Artificial Intelligence(BAAI).L.F.Z.acknowledges the support of the BAAI.We are also grateful for Dr.Wanrun Jiang,Fengbo Yuan,and Denghui Lu for helpful discussions on the training,free energy calculations,and model compression.
文摘Large scale atomistic simulations provide direct access to important materials phenomena not easily accessible to experiments or quantum mechanics-based calculation approaches.Accurate and efficient interatomic potentials are the key enabler,but their development remains a challenge for complex materials and/or complex phenomena.Machine learning potentials,such as the Deep Potential(DP)approach,provide robust means to produce general purpose interatomic potentials.Here,we provide a methodology for specialising machine learning potentials for high fidelity simulations of complex phenomena,where general potentials do not suffice.As an example,we specialise a general purpose DP method to describe the mechanical response of two allotropes of titanium(in addition to other defect,thermodynamic and structural properties).The resulting DP correctly captures the structures,energies,elastic constants andγ-lines of Ti in both the HCP and BCC structures,as well as properties such as dislocation core structures,vacancy formation energies,phase transition temperatures,and thermal expansion.The DP thus enables direct atomistic modelling of plastic and fracture behaviour of Ti.The approach to specialising DP interatomic potential,DPspecX,for accurate reproduction of properties of interest“X”,is general and extensible to other systems and properties.
基金T W and D J S gratefully acknowledge the support of the Research Grants Council,Hong Kong SAR,through the Collaborative Research Fund Project No.8730054The work of H W is supported by the National Science Foundation of China under Grant Nos.11871110 and 12122103The work of W E is supported in part by a gift from iFlytek to Princeton University。
文摘To fill the gap between accurate(and expensive)ab initio calculations and efficient atomistic simulations based on empirical interatomic potentials,a new class of descriptions of atomic interactions has emerged and been widely applied;i.e.machine learning potentials(MLPs).One recently developed type of MLP is the deep potential(DP)method.In this review,we provide an introduction to DP methods in computational materials science.The theory underlying the DP method is presented along with a step-by-step introduction to their development and use.We also review materials applications of DPs in a wide range of materials systems.The DP Library provides a platform for the development of DPs and a database of extant DPs.We discuss the accuracy and efficiency of DPs compared with ab initio methods and empirical potentials.
