Next generation interatomic potentials for condensed systems

被引:61
作者
Handley, Christopher Michael [1 ]
Behler, Joerg [1 ]
机构
[1] Ruhr Univ Bochum, Lehrstuhl Theoret Chem, D-44780 Bochum, Germany
关键词
LEAST-SQUARES METHODS; REACTIVE FORCE-FIELD; MOLECULAR-DYNAMICS SIMULATIONS; MODIFIED SHEPARD INTERPOLATION; DIPOLE-MOMENT SURFACES; EMBEDDED-ATOM METHOD; TIGHT-BINDING METHOD; ENERGY SURFACES; NEURAL-NETWORKS; GENETIC ALGORITHMS;
D O I
10.1140/epjb/e2014-50070-0
中图分类号
O469 [凝聚态物理学];
学科分类号
070205 ;
摘要
The computer simulation of condensed systems is a challenging task. While electronic structure methods like density-functional theory (DFT) usually provide a good compromise between accuracy and efficiency, they are computationally very demanding and thus applicable only to systems containing up to a few hundred atoms. Unfortunately, many interesting problems require simulations to be performed on much larger systems involving thousands of atoms or more. Consequently, more efficient methods are urgently needed, and a lot of effort has been spent on the development of a large variety of potentials enabling simulations with significantly extended time and length scales. Most commonly, these potentials are based on physically motivated functional forms and thus perform very well for the applications they have been designed for. On the other hand, they are often highly system-specific and thus cannot easily be transferred from one system to another. Moreover, their numerical accuracy is restricted by the intrinsic limitations of the imposed functional forms. In recent years, several novel types of potentials have emerged, which are not based on physical considerations. Instead, they aim to reproduce a set of reference electronic structure data as accurately as possible by using very general and flexible functional forms. In this review we will survey a number of these methods. While they differ in the choice of the employed mathematical functions, they all have in common that they provide high-quality potential-energy surfaces, while the efficiency is comparable to conventional empirical potentials. It has been demonstrated that in many cases these potentials now offer a very interesting new approach to study complex systems with hitherto unreached accuracy.
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页数:16
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  • [1] Allinger N. L., 1976, ADV PHYS ORG CHEM, V13, P1, DOI 10.1016/S0065-3160(08)60212-9
  • [2] [Anonymous], 1986, Curve and Surface Fitting
  • [3] Neural network potentials for metals and oxides - First applications to copper clusters at zinc oxide
    Artrith, Nongnuch
    Hiller, Bjoern
    Behler, Joerg
    [J]. PHYSICA STATUS SOLIDI B-BASIC SOLID STATE PHYSICS, 2013, 250 (06): : 1191 - 1203
  • [4] High-dimensional neural network potentials for metal surfaces: A prototype study for copper
    Artrith, Nongnuch
    Behler, Joerg
    [J]. PHYSICAL REVIEW B, 2012, 85 (04)
  • [5] High-dimensional neural-network potentials for multicomponent systems: Applications to zinc oxide
    Artrith, Nongnuch
    Morawietz, Tobias
    Behler, Joerg
    [J]. PHYSICAL REVIEW B, 2011, 83 (15):
  • [6] Support vector machine regression (LS-SVM)-an alternative to artificial neural networks (ANNs) for the analysis of quantum chemistry data?
    Balabin, Roman M.
    Lomakina, Ekaterina I.
    [J]. PHYSICAL CHEMISTRY CHEMICAL PHYSICS, 2011, 13 (24) : 11710 - 11718
  • [7] Machine-learning approach for one- and two-body corrections to density functional theory: Applications to molecular and condensed water
    Bartok, Albert P.
    Gillan, Michael J.
    Manby, Frederick R.
    Csanyi, Gabor
    [J]. PHYSICAL REVIEW B, 2013, 88 (05)
  • [8] On representing chemical environments
    Bartok, Albert P.
    Kondor, Risi
    Csanyi, Gabor
    [J]. PHYSICAL REVIEW B, 2013, 87 (18)
  • [9] Gaussian Approximation Potentials: The Accuracy of Quantum Mechanics, without the Electrons
    Bartok, Albert P.
    Payne, Mike C.
    Kondor, Risi
    Csanyi, Gabor
    [J]. PHYSICAL REVIEW LETTERS, 2010, 104 (13)