Details

Autor(en) / Beteiligte

• Behler, Jörg

Titel

Constructing high-dimensional neural network potentials: A tutorial review

Ist Teil von

• International journal of quantum chemistry, 2015-08, Vol.115 (16), p.1032-1050

Ort / Verlag

Hoboken: Blackwell Publishing Ltd

Erscheinungsjahr

2015

Link zum Volltext

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• A lot of progress has been made in recent years in the development of atomistic potentials using machine learning (ML) techniques. In contrast to most conventional potentials, which are based on physical approximations and simplifications to derive an analytic functional relation between the atomic configuration and the potential‐energy, ML potentials rely on simple but very flexible mathematical terms without a direct physical meaning. Instead, in case of ML potentials the topology of the potential‐energy surface is “learned” by adjusting a number of parameters with the aim to reproduce a set of reference electronic structure data as accurately as possible. Due to this bias‐free construction, they are applicable to a wide range of systems without changes in their functional form, and a very high accuracy close to the underlying first‐principles data can be obtained. Neural network potentials (NNPs), which have first been proposed about two decades ago, are an important class of ML potentials. Although the first NNPs have been restricted to small molecules with only a few degrees of freedom, they are now applicable to high‐dimensional systems containing thousands of atoms, which enables addressing a variety of problems in chemistry, physics, and materials science. In this tutorial review, the basic ideas of NNPs are presented with a special focus on developing NNPs for high‐dimensional condensed systems. A recipe for the construction of these potentials is given and remaining limitations of the method are discussed. © 2015 Wiley Periodicals, Inc. In this tutorial review, a method to construct high‐dimensional interatomic potentials employing artificial neural networks is reviewed. This approach allows one to carry out molecular dynamics simulations of large systems containing thousands of atoms with close to first‐principles accuracy and has been applied successfully to a number of different systems including metals, semiconductors, oxides, and molecular clusters. A strong focus of the review is on practical aspects of constructing these potentials.