Long-Timescale Simulations: Challenges, Pitfalls, Best Practices, for Development and Applications

Graeme Henkelman (1) , Hannes Jónsson (2) , Tony Lelièvre (3, 4) , Normand Mousseau (5) , Arthur Voter (6)

1 University of Texas at Austin [Austin]
2 University of Iceland [Reykjavik]
3 CERMICS - Centre d'Enseignement et de Recherche en Mathématiques et Calcul Scientifique
4 MATHERIALS - MATHematics for MatERIALS
5 UdeM - Université de Montréal
6 LANL - Los Alamos National Laboratory

In this chapter, we examine the practice of developing, implementing, and applying long-timescale simulation methods. In contrast to standard molecular dynamics, the performance, and sometimes the accuracy, of long-timescale atomistic methods can vary greatly from one application to another. Therefore, for the practitioners, it is particularly important to understand the strengths and weaknesses of the methods, in order to assess their respective potential for specific problems, as well as maximize their efficiency. For the method developer, clearly assessing the challenges faced by current methods as well as the areas of opportunities for future development is paramount. In the following, we present the opinion of five leaders in the field regarding best practices, challenges, and pitfalls in the use and development of such methods. Their answers both provide a roadmap to how best to approach the field and deliver insight into areas that need addressing in the future.

Domaines: Analyse numérique [math.NA]

Graeme Henkelman, Hannes Jónsson, Tony Lelièvre, Normand Mousseau, Arthur Voter. Long-Timescale Simulations: Challenges, Pitfalls, Best Practices, for Development and Applications. Handbook of Materials Modeling, Springer International Publishing, pp.1-10, 2018, ⟨10.1007/978-3-319-42913-7_31-1⟩. ⟨hal-02385706⟩